diff --git a/files/Iris_dataset/A_little_extra_information_about_the_Iris_data_set.pdf b/files/Iris_dataset/A_little_extra_information_about_the_Iris_data_set.pdf deleted file mode 100644 index e876b27..0000000 Binary files a/files/Iris_dataset/A_little_extra_information_about_the_Iris_data_set.pdf and /dev/null differ diff --git a/files/Iris_dataset/README..txt b/files/Iris_dataset/README..txt deleted file mode 100644 index 062b486..0000000 --- a/files/Iris_dataset/README..txt +++ /dev/null @@ -1,69 +0,0 @@ -1. Title: Iris Plants Database - Updated Sept 21 by C.Blake - Added discrepency information - -2. Sources: - (a) Creator: R.A. Fisher - (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) - (c) Date: July, 1988 - -3. Past Usage: - - Publications: too many to mention!!! Here are a few. - 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems" - Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions - to Mathematical Statistics" (John Wiley, NY, 1950). - 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. - (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. - 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System - Structure and Classification Rule for Recognition in Partially Exposed - Environments". IEEE Transactions on Pattern Analysis and Machine - Intelligence, Vol. PAMI-2, No. 1, 67-71. - -- Results: - -- very low misclassification rates (0% for the setosa class) - 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE - Transactions on Information Theory, May 1972, 431-433. - -- Results: - -- very low misclassification rates again - 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II - conceptual clustering system finds 3 classes in the data. - -4. Relevant Information: - --- This is perhaps the best known database to be found in the pattern - recognition literature. Fisher's paper is a classic in the field - and is referenced frequently to this day. (See Duda & Hart, for - example.) The data set contains 3 classes of 50 instances each, - where each class refers to a type of iris plant. One class is - linearly separable from the other 2; the latter are NOT linearly - separable from each other. - --- Predicted attribute: class of iris plant. - --- This is an exceedingly simple domain. - --- This data differs from the data presented in Fishers article - (identified by Steve Chadwick, spchadwick@espeedaz.net ) - The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" - where the error is in the fourth feature. - The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" - where the errors are in the second and third features. - -5. Number of Instances: 150 (50 in each of three classes) - -6. Number of Attributes: 4 numeric, predictive attributes and the class - -7. Attribute Information: - 1. sepal length in cm - 2. sepal width in cm - 3. petal length in cm - 4. petal width in cm - 5. class: - -- Iris Setosa - -- Iris Versicolour - -- Iris Virginica - -8. Missing Attribute Values: None - -Summary Statistics: - Min Max Mean SD Class Correlation - sepal length: 4.3 7.9 5.84 0.83 0.7826 - sepal width: 2.0 4.4 3.05 0.43 -0.4194 - petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) - petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) - -9. Class Distribution: 33.3% for each of 3 classes. diff --git a/files/Iris_dataset/data_IRIS_setosa-vs-versicolor_3-col.csv b/files/Iris_dataset/data_IRIS_setosa-vs-versicolor_3-col.csv deleted file mode 100644 index 6054517..0000000 --- a/files/Iris_dataset/data_IRIS_setosa-vs-versicolor_3-col.csv +++ /dev/null @@ -1,101 +0,0 @@ -a,b,c,s -5.1,3.5,1.4,0 -4.9,3,1.4,0 -4.7,3.2,1.3,0 -4.6,3.1,1.5,0 -5,3.6,1.4,0 -5.4,3.9,1.7,0 -4.6,3.4,1.4,0 -5,3.4,1.5,0 -4.4,2.9,1.4,0 -4.9,3.1,1.5,0 -5.4,3.7,1.5,0 -4.8,3.4,1.6,0 -4.8,3,1.4,0 -4.3,3,1.1,0 -5.8,4,1.2,0 -5.7,4.4,1.5,0 -5.4,3.9,1.3,0 -5.1,3.5,1.4,0 -5.7,3.8,1.7,0 -5.1,3.8,1.5,0 -5.4,3.4,1.7,0 -5.1,3.7,1.5,0 -4.6,3.6,1,0 -5.1,3.3,1.7,0 -4.8,3.4,1.9,0 -5,3,1.6,0 -5,3.4,1.6,0 -5.2,3.5,1.5,0 -5.2,3.4,1.4,0 -4.7,3.2,1.6,0 -4.8,3.1,1.6,0 -5.4,3.4,1.5,0 -5.2,4.1,1.5,0 -5.5,4.2,1.4,0 -4.9,3.1,1.5,0 -5,3.2,1.2,0 -5.5,3.5,1.3,0 -4.9,3.6,1.4,0 -4.4,3,1.3,0 -5.1,3.4,1.5,0 -5,3.5,1.3,0 -4.5,2.3,1.3,0 -4.4,3.2,1.3,0 -5,3.5,1.6,0 -5.1,3.8,1.9,0 -4.8,3,1.4,0 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-6.5,3.0,5.2,2.0,1 -6.2,3.4,5.4,2.3,1 -5.9,3.0,5.1,1.8,1 diff --git a/files/Iris_dataset/iris_data_corrected.csv b/files/Iris_dataset/iris_data_corrected.csv deleted file mode 100644 index 77ac460..0000000 --- a/files/Iris_dataset/iris_data_corrected.csv +++ /dev/null @@ -1,150 +0,0 @@ -5.1,3.5,1.4,0.2,Iris-setosa -4.9,3,1.4,0.2,Iris-setosa -4.7,3.2,1.3,0.2,Iris-setosa -4.6,3.1,1.5,0.2,Iris-setosa -5,3.6,1.4,0.2,Iris-setosa -5.4,3.9,1.7,0.4,Iris-setosa -4.6,3.4,1.4,0.3,Iris-setosa -5,3.4,1.5,0.2,Iris-setosa -4.4,2.9,1.4,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -5.4,3.7,1.5,0.2,Iris-setosa -4.8,3.4,1.6,0.2,Iris-setosa -4.8,3,1.4,0.1,Iris-setosa -4.3,3,1.1,0.1,Iris-setosa -5.8,4,1.2,0.2,Iris-setosa -5.7,4.4,1.5,0.4,Iris-setosa -5.4,3.9,1.3,0.4,Iris-setosa -5.1,3.5,1.4,0.3,Iris-setosa -5.7,3.8,1.7,0.3,Iris-setosa -5.1,3.8,1.5,0.3,Iris-setosa -5.4,3.4,1.7,0.2,Iris-setosa -5.1,3.7,1.5,0.4,Iris-setosa -4.6,3.6,1,0.2,Iris-setosa -5.1,3.3,1.7,0.5,Iris-setosa -4.8,3.4,1.9,0.2,Iris-setosa 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5c4316c..0000000 --- a/files/Iris_dataset/iris_data_original.csv +++ /dev/null @@ -1,151 +0,0 @@ -5.1,3.5,1.4,0.2,Iris-setosa -4.9,3.0,1.4,0.2,Iris-setosa -4.7,3.2,1.3,0.2,Iris-setosa -4.6,3.1,1.5,0.2,Iris-setosa -5.0,3.6,1.4,0.2,Iris-setosa -5.4,3.9,1.7,0.4,Iris-setosa -4.6,3.4,1.4,0.3,Iris-setosa -5.0,3.4,1.5,0.2,Iris-setosa -4.4,2.9,1.4,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -5.4,3.7,1.5,0.2,Iris-setosa -4.8,3.4,1.6,0.2,Iris-setosa -4.8,3.0,1.4,0.1,Iris-setosa -4.3,3.0,1.1,0.1,Iris-setosa -5.8,4.0,1.2,0.2,Iris-setosa -5.7,4.4,1.5,0.4,Iris-setosa -5.4,3.9,1.3,0.4,Iris-setosa -5.1,3.5,1.4,0.3,Iris-setosa -5.7,3.8,1.7,0.3,Iris-setosa -5.1,3.8,1.5,0.3,Iris-setosa -5.4,3.4,1.7,0.2,Iris-setosa -5.1,3.7,1.5,0.4,Iris-setosa -4.6,3.6,1.0,0.2,Iris-setosa -5.1,3.3,1.7,0.5,Iris-setosa -4.8,3.4,1.9,0.2,Iris-setosa -5.0,3.0,1.6,0.2,Iris-setosa -5.0,3.4,1.6,0.4,Iris-setosa -5.2,3.5,1.5,0.2,Iris-setosa -5.2,3.4,1.4,0.2,Iris-setosa -4.7,3.2,1.6,0.2,Iris-setosa -4.8,3.1,1.6,0.2,Iris-setosa 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@@ -coefficients -.1 -.2 -.3 -.4 -.5 -1 -2 -3 -4 -5 diff --git a/files/data_CLASSIFY.csv b/files/data_CLASSIFY.csv deleted file mode 100644 index 20e308c..0000000 --- a/files/data_CLASSIFY.csv +++ /dev/null @@ -1,151 +0,0 @@ -sl,sw,pl,pw,s -5.1,3.5,1.4,0.2,0 -4.9,3,1.4,0.2,0 -4.7,3.2,1.3,0.2,0 -4.6,3.1,1.5,0.2,0 -5,3.6,1.4,0.2,0 -5.4,3.9,1.7,0.4,0 -4.6,3.4,1.4,0.3,0 -5,3.4,1.5,0.2,0 -4.4,2.9,1.4,0.2,0 -4.9,3.1,1.5,0.1,0 -5.4,3.7,1.5,0.2,0 -4.8,3.4,1.6,0.2,0 -4.8,3,1.4,0.1,0 -4.3,3,1.1,0.1,0 -5.8,4,1.2,0.2,0 -5.7,4.4,1.5,0.4,0 -5.4,3.9,1.3,0.4,0 -5.1,3.5,1.4,0.3,0 -5.7,3.8,1.7,0.3,0 -5.1,3.8,1.5,0.3,0 -5.4,3.4,1.7,0.2,0 -5.1,3.7,1.5,0.4,0 -4.6,3.6,1,0.2,0 -5.1,3.3,1.7,0.5,0 -4.8,3.4,1.9,0.2,0 -5,3,1.6,0.2,0 -5,3.4,1.6,0.4,0 -5.2,3.5,1.5,0.2,0 -5.2,3.4,1.4,0.2,0 -4.7,3.2,1.6,0.2,0 -4.8,3.1,1.6,0.2,0 -5.4,3.4,1.5,0.4,0 -5.2,4.1,1.5,0.1,0 -5.5,4.2,1.4,0.2,0 -4.9,3.1,1.5,0.2,0 -5,3.2,1.2,0.2,0 -5.5,3.5,1.3,0.2,0 -4.9,3.6,1.4,0.1,0 -4.4,3,1.3,0.2,0 -5.1,3.4,1.5,0.2,0 -5,3.5,1.3,0.3,0 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b/files/data_MATCH.csv deleted file mode 100644 index c071d5e..0000000 --- a/files/data_MATCH.csv +++ /dev/null @@ -1,6 +0,0 @@ -a,b,c,s -0,1,2,3 -1,2,3,6 -2,3,4,9 -3,4,5,12 -4,5,6,15 diff --git a/files/data_PLAY.csv b/files/data_PLAY.csv deleted file mode 100644 index c071d5e..0000000 --- a/files/data_PLAY.csv +++ /dev/null @@ -1,6 +0,0 @@ -a,b,c,s -0,1,2,3 -1,2,3,6 -2,3,4,9 -3,4,5,12 -4,5,6,15 diff --git a/files/data_REGRESS.csv b/files/data_REGRESS.csv deleted file mode 100644 index e72e0a8..0000000 --- a/files/data_REGRESS.csv +++ /dev/null @@ -1,10 +0,0 @@ -t,r,s -0.241,0.39,0.98 -.615,0.72,1.01 -1.00,1.00,1.00 -1.88,1.52,1.01 -11.8,5.20,0.99 -29.5,9.54,1.00 -84.0,19.18,1.00 -165,30.06,1.00 -248,39.44,1.00 diff --git a/files/operators_CLASSIFY.csv b/files/operators_CLASSIFY.csv deleted file mode 100644 index 33f6af0..0000000 --- a/files/operators_CLASSIFY.csv +++ /dev/null @@ -1,5 +0,0 @@ -operator, arity -+,2 --,2 -*,2 -/,2 diff --git a/files/operators_MATCH.csv b/files/operators_MATCH.csv deleted file mode 100644 index 33f6af0..0000000 --- a/files/operators_MATCH.csv +++ /dev/null @@ -1,5 +0,0 @@ -operator, arity -+,2 --,2 -*,2 -/,2 diff --git a/files/operators_PLAY.csv b/files/operators_PLAY.csv deleted file mode 100644 index 33f6af0..0000000 --- a/files/operators_PLAY.csv +++ /dev/null @@ -1,5 +0,0 @@ -operator, arity -+,2 --,2 -*,2 -/,2 diff --git a/files/operators_REGRESS.csv b/files/operators_REGRESS.csv deleted file mode 100644 index 33f6af0..0000000 --- a/files/operators_REGRESS.csv +++ /dev/null @@ -1,5 +0,0 @@ -operator, arity -+,2 --,2 -*,2 -/,2 diff --git a/files/templates/data_iris_2-class.csv b/files/templates/data_iris_2-class.csv deleted file mode 100644 index 6054517..0000000 --- a/files/templates/data_iris_2-class.csv +++ /dev/null @@ -1,101 +0,0 @@ -a,b,c,s -5.1,3.5,1.4,0 -4.9,3,1.4,0 -4.7,3.2,1.3,0 -4.6,3.1,1.5,0 -5,3.6,1.4,0 -5.4,3.9,1.7,0 -4.6,3.4,1.4,0 -5,3.4,1.5,0 -4.4,2.9,1.4,0 -4.9,3.1,1.5,0 -5.4,3.7,1.5,0 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a/files/templates/data_power.csv b/files/templates/data_power.csv deleted file mode 100644 index f55183e..0000000 --- a/files/templates/data_power.csv +++ /dev/null @@ -1,6 +0,0 @@ -a,b,s -1,2,1 -2,2,4 -3,2,9 -4,2,16 -5,2,25 diff --git a/files/templates/data_sqrt.csv b/files/templates/data_sqrt.csv deleted file mode 100644 index 6ee03bf..0000000 --- a/files/templates/data_sqrt.csv +++ /dev/null @@ -1,10 +0,0 @@ -a,b,s -4,10,20 -9,10,30 -16,10,40 -25,10,50 -36,10,60 -49,10,70 -64,10,80 -81,10,90 -100,10,100 diff --git a/files/templates/data_sum.csv b/files/templates/data_sum.csv deleted file mode 100644 index 20ada99..0000000 --- a/files/templates/data_sum.csv +++ /dev/null @@ -1,11 +0,0 @@ -a,b,c,s -0,1,2,3 -1,2,3,6 -2,3,4,9 -3,4,5,12 -4,5,6,15 -5,6,7,18 -6,7,8,21 -7,8,9,24 -8,9,10,27 -9,10,11,30 diff --git a/files/templates/data_trig.csv b/files/templates/data_trig.csv deleted file mode 100644 index b7bdb1c..0000000 --- a/files/templates/data_trig.csv +++ /dev/null @@ -1,4 +0,0 @@ -a,b,c,s -1,2,45,0.5253219888 -2,0,22,-1.9999216528 -10,3,-5,2.8366218546 diff --git a/files/templates/operators_list.txt b/files/templates/operators_list.txt deleted file mode 100644 index 24193fd..0000000 --- a/files/templates/operators_list.txt +++ /dev/null @@ -1,40 +0,0 @@ -OPERATORS TESTED with Karoo GP v1.0 - -+,2 --,2 -*,2 -/,2 -**,2 - -The following are not currently supported, but will be again with subsequent releases. - -and,2 -or,2 - -+ sin,2 -- sin,2 -* sin,2 -/ sin,2 - -+ cos,2 -- cos,2 -* cos,2 -/ cos,2 - -+ exp,2 -- exp,2 -* exp,2 -/ exp,2 - -+ sqrt,2 -+ sqrt,2 -- sqrt,2 -/ sqrt,2 - -+ log,2 -- log,2 -* log,2 -/ log,2 - - -Where sin, cos, exp, and log must be preceded by another operator. diff --git a/karoo_gp/Karoo_GP_User_Guide.pdf b/karoo_gp/Karoo_GP_User_Guide.pdf deleted file mode 100644 index 26e7069..0000000 Binary files a/karoo_gp/Karoo_GP_User_Guide.pdf and /dev/null differ diff --git a/karoo_gp/karoo_gp_base_class.py b/karoo_gp/karoo_gp_base_class.py deleted file mode 100644 index 9d014d4..0000000 --- a/karoo_gp/karoo_gp_base_class.py +++ /dev/null @@ -1,2700 +0,0 @@ -# Karoo GP Base Class -# Define the methods and global variables used by Karoo GP -# by Kai Staats, MSc; see LICENSE.md -# Thanks to Emmanuel Dufourq and Arun Kumar for support during 2014-15 devel; TensorFlow support provided by Iurii Milovanov -# version 1.0.3 - -''' -A NOTE TO THE NEWBIE, EXPERT, AND BRAVE -Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo User Guide' before running -this application. While your computer will not burst into flames nor will the sun collapse into a black hole if you do not, you will -likely find more enjoyment of this particular flavour of GP with a little understanding of its intent and design. -''' - -import sys -import os -import csv -import time - -import numpy as np -import sklearn.metrics as skm -import sklearn.cross_validation as skcv - -from sympy import sympify -from datetime import datetime -from collections import OrderedDict - -# TensorFlow-related imports -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1" -import tensorflow as tf -import ast -import operator as op -operators = {ast.Add: op.add, ast.Sub: op.sub, ast.Mult: op.mul, ast.Div: op.truediv, ast.Pow: op.pow, ast.BitXor: op.xor, ast.USub: op.neg} - -np.set_printoptions(linewidth = 320) # set the terminal to print 320 characters before line-wrapping in order to view Trees - - -class Base_GP(object): - - ''' - This Base_BP class contains all methods for Karoo GP. - - Method names are differentiated from global variable names (defined below) by the prefix 'fx_' followed by an object - and action, as in 'fx_display_tree()', with a few expections, such as 'fx_fitness_gene_pool'. - - The categories (denoted by +++ banners +++) are as follows: - 'karoo_gp' A single method which conducts an entire run. Employed only by karoo_gp_server.py - 'fx_karoo_' Methods to Run Karoo GP - 'fx_gen_' Methods to Generate a Tree - 'fx_eval_' Methods to Evaluate a Tree - 'fx_fitness_' Methods to Train and Test a Tree for Fitness - 'fx_evolve_' Methods to Evolve a Population - 'fx_display_' Methods to Display a Tree - 'fx_archive_' Methods to Archive - - There are no sub-classes at the time of this edit - 2015 09/21 - ''' - - #++++++++++++++++++++++++++++++++++++++++++ - # Define Global Variables | - #++++++++++++++++++++++++++++++++++++++++++ - - def __init__(self): - - ''' - All Karoo GP global variables are named with the prefix 'gp.' The 13 variables which begin with 'gp.pop_' are - specifically employed to define the 13 parameters for each tree as stored in the axis-1 (expand horizontally) - 'gp.population' Numpy array. - - ### Global and local variables defined by the user in karoo_gp_main.py (in order of appearence) ### - 'gp.kernel' fitness function - 'gp.class_method' select the number of classes (will be automated in future version) - 'tree_type' Full, Grow, or Ramped 50/50 (local variable) - 'gp.tree_depth_min' minimum number of nodes - 'tree_depth_base' maximum Tree depth for the initial population, where nodes = 2^(depth + 1) - 1 - 'gp.tree_depth_max' maximum Tree depth for the entire run; introduces potential bloat - 'gp.tree_pop_max' maximum number of Trees per generation - 'gp.generation_max' maximum number of generations - 'gp.display' level of on-screen feedback - - 'gp.evolve_repro' quantity of a population generated through Reproduction - 'gp.evolve_point' quantity of a population generated through Point Mutation - 'gp.evolve_branch' quantity of a population generated through Branch Mutation - 'gp.evolve_cross' quantity of a population generated through Crossover - - 'gp.tourn_size' the number of Trees chosen for each tournament - 'gp.precision' the number of floating points for all applications of the round function - - ### Global variables used for data management ### - 'gp.data_train' store train data for processing in TF - 'gp.data_test' store test data for processing in TF - 'gp.tf_device' set TF computation backend device (CPU or GPU) - 'gp.tf_device_log' employed for TensorFlow debugging - - 'gp.data_train_cols' number of cols in the TRAINING data (see 'fx_karoo_data_load', below) - 'gp.data_train_rows' number of rows in the TRAINING data (see 'fx_karoo_data_load', below) - 'gp.data_test_cols' number of cols in the TEST data (see 'fx_karoo_data_load', below) - 'gp.data_test_rows' number of rows in the TEST data (see 'fx_karoo_data_load', below) - - 'gp.functions' user defined functions (operators) from the associated files/[functions].csv - 'gp.terminals' user defined variables (operands) from the top row of the associated [data].csv - 'gp.coeff' user defined coefficients (NOT YET IN USE) - 'gp.fitness_type' fitness type - 'gp.datetime' date-time stamp of when the unique directory is created - 'gp.path' full path to the unique directory created with each run - 'gp.dataset' local path and dataset filename - - ### Global variables initiated and/or used by Sympy ### - 'gp.algo_raw' a Sympy string which represents a flattened tree - 'gp.algo_sym' a Sympy executable version of algo_raw - 'gp.fittest_dict' a dictionary of the most fit trees, compiled during fitness function execution - - ### Variables used for evolutionary management ### - 'gp.population_a' the root generation from which Trees are chosen for mutation and reproduction - 'gp.population_b' the generation constructed from gp.population_a (recyled) - 'gp.gene_pool' once-per-generation assessment of trees that meet min and max boundary conditions - 'gp.generation_id' simple n + 1 increment - 'gp.fitness_type' set in 'fx_karoo_data_load' as either a minimising or maximising function - 'gp.tree' axis-1, 13 element Numpy array that defines each Tree, stored in 'gp.population' - 'gp.pop_*' 13 elements which define each Tree (see 'fx_gen_tree_initialise' below) - - ### Fishing nets ### - You can insert a "fishing net" to search for a specific expression when you fear the evolutionary process or - something in the code may not be working. Search for "fishing net" and follow the directions. - - ### Error checks ### - You can quickly find all places in which error checks have been inserted by searching for "ERROR!" - ''' - - self.algo_raw = [] # temp store the raw expression -- CONSIDER MAKING THIS VARIABLE LOCAL - self.algo_sym = [] # temp store the sympified expression-- CONSIDER MAKING THIS VARIABLE LOCAL - self.fittest_dict = {} # temp store all Trees which share the best fitness score - self.gene_pool = [] # temp store all Tree IDs for use by Tournament - self.class_labels = 0 # temp set a variable which will be assigned the number of class labels (data_y) - - return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Run Karoo GP | - #++++++++++++++++++++++++++++++++++++++++++ - - def karoo_gp(self, tree_type, tree_depth_base, filename): - - ''' - This method enables the engagement of the entire Karoo GP application. It is used exclusively by the server script - karoo_gp_server.py (not by the desktop script karoo_gp_main.py). Instead of returning the user to the pause menu, - this script terminates at the command-line, providing support for bash and chron job execution. - - Arguments required: tree_type, tree_depth_base, filename - ''' - - self.karoo_banner() - start = time.time() # start the clock for the timer - - # construct first generation of Trees - self.fx_karoo_data_load(tree_type, tree_depth_base, filename) - self.generation_id = 1 # set initial generation ID - self.population_a = ['Karoo GP by Kai Staats, Generation ' + str(self.generation_id)] # list to store all Tree arrays, one generation at a time - self.fx_karoo_construct(tree_type, tree_depth_base) # construct the first population of Trees - - # evaluate first generation of Trees - print '\n Evaluate the first generation of Trees ...' - self.fx_fitness_gym(self.population_a) # generate expression, evaluate fitness, compare fitness - self.fx_archive_tree_write(self.population_a, 'a') # save the first generation of Trees to disk - - # evolve subsequent generations of Trees - for self.generation_id in range(2, self.generation_max + 1): # loop through 'generation_max' - - print '\n Evolve a population of Trees for Generation', self.generation_id, '...' - self.population_b = ['Karoo GP by Kai Staats, Evolving Generation'] # initialise population_b to host the next generation - - self.fx_fitness_gene_pool() # generate the viable gene pool (compares against gp.tree_depth_min) - self.fx_karoo_reproduce() # method 1 - Reproduction - self.fx_karoo_point_mutate() # method 2 - Point Mutation - self.fx_karoo_branch_mutate() # method 3 - Branch Mutation - self.fx_karoo_crossover() # method 4 - Crossover - self.fx_eval_generation() # evaluate all Trees in a single generation - - self.population_a = self.fx_evolve_pop_copy(self.population_b, ['GP Tree by Kai Staats, Generation ' + str(self.generation_id)]) - - # "End of line, man!" --CLU - print '\n \033[36m Karoo GP has an ellapsed time of \033[0;0m\033[31m%f\033[0;0m' % (time.time() - start), '\033[0;0m' - self.fx_archive_tree_write(self.population_b, 'f') # save the final generation of Trees to disk - self.fx_archive_params_write('Server') # save run-time parameters to disk - - print '\n \033[3m Congrats!\033[0;0m Your multi-generational Karoo GP run is complete.\n' - sys.exit() # return Karoo GP to the command line to support bash and chron job execution - - # return - - - def karoo_banner(self): - - ''' - This method makes Karoo GP look old-school cool! - - Arguments required: none - ''' - - os.system('clear') - - print '\n\033[36m\033[1m' - print '\t ** ** ****** ***** ****** ****** ****** ******' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t **** ******** ****** ** ** ** ** ** *** ******' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ****** ****** ****** **' - print '\033[0;0m' - print '\t\033[36m Genetic Programming in Python - by Kai Staats, version 1.0\033[0;0m' - - return - - - def fx_karoo_data_load(self, tree_type, tree_depth_base, filename): - - ''' - The data and function .csv files are loaded according to the fitness function kernel selected by the user. An - alternative dataset may be loaded at launch, by appending a command line argument. The data is then split into - both TRAINING and TEST segments in order to validate the success of the GP training run. Datasets less than - 10 rows will not be split, rather copied in full to both TRAINING and TEST as it is assumed you are conducting - a system validation run, as with the built-in MATCH kernel and associated dataset. - - Arguments required: tree_type, tree_depth_base, filename (of the dataset) - ''' - - ### 1) load the associated data set, operators, operands, fitness type, and coefficients ### - - full_path = os.path.realpath(__file__); cwd = os.path.dirname(full_path) # Good idea Marco :) - # cwd = os.getcwd() - - data_dict = {'c':cwd + '/files/data_CLASSIFY.csv', 'r':cwd + '/files/data_REGRESS.csv', 'm':cwd + '/files/data_MATCH.csv', 'p':cwd + '/files/data_PLAY.csv'} - - if len(sys.argv) == 1: # load data from the default karoo_gp/files/ directory - data_x = np.loadtxt(data_dict[self.kernel], skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column - data_y = np.loadtxt(data_dict[self.kernel], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) - header = open(data_dict[self.kernel],'r') - self.dataset = data_dict[self.kernel] - - elif len(sys.argv) == 2: # load an external data file - data_x = np.loadtxt(sys.argv[1], skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column - data_y = np.loadtxt(sys.argv[1], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) - header = open(sys.argv[1],'r') - self.dataset = sys.argv[1] - - elif len(sys.argv) > 2: # receive filename and additional flags from karoo_gp_server.py via argparse - data_x = np.loadtxt(filename, skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column - data_y = np.loadtxt(filename, skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) - header = open(filename,'r') - self.dataset = filename - - fitt_dict = {'c':'max', 'r':'min', 'm':'max', 'p':''} - self.fitness_type = fitt_dict[self.kernel] # load fitness type - - func_dict = {'c':cwd + '/files/operators_CLASSIFY.csv', 'r':cwd + '/files/operators_REGRESS.csv', 'm':cwd + '/files/operators_MATCH.csv', 'p':cwd + '/files/operators_PLAY.csv'} - self.functions = np.loadtxt(func_dict[self.kernel], delimiter=',', skiprows=1, dtype = str) # load the user defined functions (operators) - self.terminals = header.readline().split(','); self.terminals[-1] = self.terminals[-1].replace('\n','') # load the user defined terminals (operands) - self.class_labels = len(np.unique(data_y)) # load the user defined labels for classification or solutions for regression - self.coeff = np.loadtxt(cwd + '/files/coefficients.csv', delimiter=',', skiprows=1, dtype = str) # load the user defined coefficients - NOT USED YET - - - ### 2) from the dataset, extract TRAINING and TEST data ### - - if len(data_x) < 11: # for small datasets we will not split them into TRAINING and TEST components - data_train = np.c_[data_x, data_y] - data_test = np.c_[data_x, data_y] - - else: # if larger than 10, we run the data through the SciKit Learn's 'random split' function - x_train, x_test, y_train, y_test = skcv.train_test_split(data_x, data_y, test_size = 0.2) # 80/20 TRAIN/TEST split - data_x, data_y = [], [] # clear from memory - - data_train = np.c_[x_train, y_train] # recombine each row of data with its associated label (right column) - x_train, y_train = [], [] # clear from memory - - data_test = np.c_[x_test, y_test] # recombine each row of data with its associated label (right column) - x_test, y_test = [], [] # clear from memory - - self.data_train_cols = len(data_train[0,:]) # qty count - self.data_train_rows = len(data_train[:,0]) # qty count - self.data_test_cols = len(data_test[0,:]) # qty count - self.data_test_rows = len(data_test[:,0]) # qty count - - - ### 3) load TRAINING and TEST data for TensorFlow processing - tested 2017 02/02 - - self.data_train = data_train # Store train data for processing in TF - self.data_test = data_test # Store test data for processing in TF - self.tf_device = "/gpu:0" # Set TF computation backend device (CPU or GPU) - self.tf_device_log = False # TF device usage logging (for debugging) - - - ### 4) create a unique directory and initialise all .csv files ### - - # self.datetime = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') - self.datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') - self.path = os.path.join(cwd, 'runs/', self.datetime) # generate a unique directory name - if not os.path.isdir(self.path): os.makedirs(self.path) # make a unique directory - - self.filename = {} # a dictionary to hold .csv filenames - - self.filename.update( {'a':self.path + '/population_a.csv'} ) - target = open(self.filename['a'], 'w') # initialise the .csv file for population 'a' (foundation) - target.close() - - self.filename.update( {'b':self.path + '/population_b.csv'} ) - target = open(self.filename['b'], 'w') # initialise the .csv file for population 'b' (evolving) - target.close() - - self.filename.update( {'f':self.path + '/population_f.csv'} ) - target = open(self.filename['f'], 'w') # initialise the .csv file for the final population (test) - target.close() - - self.filename.update( {'s':self.path + '/population_s.csv'} ) - # do NOT initialise this .csv file, as it is retained for loading a previous run (recover) - - return - - - def fx_karoo_data_recover(self, population): - - ''' - This method is used to load a saved population of Trees, as invoked through the (pause) menu where population_s - replaces population_a in the /[path]/karoo_gp/runs/ directory. - - Arguments required: population size - ''' - - with open(population, 'rb') as csv_file: - target = csv.reader(csv_file, delimiter=',') - n = 0 # track row count - - for row in target: - - n = n + 1 - if n == 1: pass # skip first empty row - - elif n == 2: - self.population_a = [row] # write header to population_a - - else: - if row == []: - self.tree = np.array([[]]) # initialise Tree array - - else: - if self.tree.shape[1] == 0: - self.tree = np.append(self.tree, [row], axis = 1) # append first row to Tree - - else: - self.tree = np.append(self.tree, [row], axis = 0) # append subsequent rows to Tree - - if self.tree.shape[0] == 13: - self.population_a.append(self.tree) # append complete Tree to population list - - print self.population_a - - return - - - def fx_karoo_construct(self, tree_type, tree_depth_base): - - ''' - As used by the method 'karoo_gp', this method constructs the initial population based upon the user-defined - Tree type and initial, maximum Tree depth ('tree_depth_base'). "Ramped half/half" was defined by John Koza as - a means of building maximum diversity in the initial population. There are equal numbers of Full and Grow - methods trees, and an equal spread of Trees across depths 1 to 'tree_depth_base'. - - Arguments required: tree_type, tree_depth_base - ''' - - if self.display == 'i' or self.display == 'g': - print '\n\t Type \033[1m?\033[0;0m at any (pause) to review your options, or \033[1mENTER\033[0;0m to continue.\033[0;0m' - self.fx_karoo_pause(0) - - if tree_type == 'r': # Ramped 50/50 - - TREE_ID = 1 - for n in range(1, int((self.tree_pop_max / 2) / tree_depth_base) + 1): # split the population into equal parts - for depth in range(1, tree_depth_base + 1): # build 2 Trees ats each depth - self.fx_gen_tree_build(TREE_ID, 'f', depth) # build a Full Tree - self.fx_archive_tree_append(self.tree) # append Tree to the list 'gp.population_a' - TREE_ID = TREE_ID + 1 - - self.fx_gen_tree_build(TREE_ID, 'g', depth) # build a Grow Tree - self.fx_archive_tree_append(self.tree) # append Tree to the list 'gp.population_a' - TREE_ID = TREE_ID + 1 - - if TREE_ID < self.tree_pop_max: # eg: split 100 by 2*3 and it will produce only 96 Trees ... - for n in range(self.tree_pop_max - TREE_ID + 1): # ... so we complete the run - self.fx_gen_tree_build(TREE_ID, 'g', tree_depth_base) - self.fx_archive_tree_append(self.tree) - TREE_ID = TREE_ID + 1 - - else: pass - - else: # Full or Grow - for TREE_ID in range(1, self.tree_pop_max + 1): - self.fx_gen_tree_build(TREE_ID, tree_type, tree_depth_base) # build the 1st generation of Trees - self.fx_archive_tree_append(self.tree) - - return - - - def fx_karoo_reproduce(self): - - ''' - Through tournament selection, a single Tree from the prior generation is copied without mutation to the next - generation. This is analogous to a member of the prior generation directly entering the gene pool of the - subsequent (younger) generation. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_repro, 'Reproductions ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_repro): # quantity of Trees to be copied without mutation - tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each reproduction - tourn_winner = self.fx_evolve_fitness_wipe(tourn_winner) # wipe fitness data - self.population_b.append(tourn_winner) # append array to next generation population of Trees - - return - - - def fx_karoo_point_mutate(self): - - ''' - Through tournament selection, a copy of a Tree from the prior generation mutates before being added to the - next generation. In this method, a single point is selected for mutation while maintaining function nodes as - functions (operators) and terminal nodes as terminals (variables). The size and shape of the Tree will remain - identical. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_point, 'Point Mutations ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_point): # quantity of Trees to be generated through mutation - tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each mutation - tourn_winner, node = self.fx_evolve_point_mutate(tourn_winner) # perform point mutation; return single point for record keeping - self.population_b.append(tourn_winner) # append array to next generation population of Trees - - return - - - def fx_karoo_branch_mutate(self): - - ''' - Through tournament selection, a copy of a Tree from the prior generation mutates before being added to the - next generation. Unlike Point Mutation, in this method an entire branch is selected. If the evolutionary run is - designated as Full, the size and shape of the Tree will remain identical, each node mutated sequentially, where - functions remain functions and terminals remain terminals. If the evolutionary run is designated as Grow or - Ramped Half/Half, the size and shape of the Tree may grow smaller or larger, but it may not exceed - tree_depth_max as defined by the user. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_branch, 'Full or Grow Mutations ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_branch): # quantity of Trees to be generated through mutation - tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each mutation - branch = self.fx_evolve_branch_select(tourn_winner) # select point of mutation and all nodes beneath - - # TEST & DEBUG: comment the top or bottom to force all Full or all Grow methods - - if tourn_winner[1][1] == 'f': # perform Full method mutation on 'tourn_winner' - tourn_winner = self.fx_evolve_full_mutate(tourn_winner, branch) - - elif tourn_winner[1][1] == 'g': # perform Grow method mutation on 'tourn_winner' - tourn_winner = self.fx_evolve_grow_mutate(tourn_winner, branch) - - self.population_b.append(tourn_winner) # append array to next generation population of Trees - - return - - - def fx_karoo_crossover(self): - - ''' - Through tournament selection, two trees are selected as parents to produce two offspring. Within each parent - Tree a branch is selected. Parent A is copied, with its selected branch deleted. Parent B's branch is then - copied to the former location of Parent A's branch and inserted (grafted). The size and shape of the child - Tree may be smaller or larger than either of the parents, but may not exceed 'tree_depth_max' as defined - by the user. - - This process combines genetic code from two parent Trees, both of which were chosen by the tournament process - as having a higher fitness than the average population. Therefore, there is a chance their offspring will - provide an improvement in total fitness. In most GP applications, Crossover is the most commonly applied - evolutionary operator (~70-80%). - - For those who like to watch, select 'db' (debug mode) at the launch of Karoo GP or at any (pause). - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_cross, 'Crossovers ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_cross / 2): # quantity of Trees to be generated through Crossover, accounting for 2 children each - - parent_a = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for 'parent_a' - branch_a = self.fx_evolve_branch_select(parent_a) # select branch within 'parent_a', to copy to 'parent_b' and receive a branch from 'parent_b' - - parent_b = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for 'parent_b' - branch_b = self.fx_evolve_branch_select(parent_b) # select branch within 'parent_b', to copy to 'parent_a' and receive a branch from 'parent_a' - - parent_c = np.copy(parent_a); branch_c = np.copy(branch_a) # else the Crossover mods affect the parent Trees, due to how Python manages '=' - parent_d = np.copy(parent_b); branch_d = np.copy(branch_b) # else the Crossover mods affect the parent Trees, due to how Python manages '=' - - offspring_1 = self.fx_evolve_crossover(parent_a, branch_a, parent_b, branch_b) # perform Crossover - self.population_b.append(offspring_1) # append the 1st child to next generation of Trees - - offspring_2 = self.fx_evolve_crossover(parent_d, branch_d, parent_c, branch_c) # perform Crossover - self.population_b.append(offspring_2) # append the 2nd child to next generation of Trees - - return - - - def fx_karoo_pause(self, eol): - - ''' - Pause the program execution and output to screen until the user selects a valid option. The "eol" parameter - instructs this method to display a different screen for run-time or end-of-line, and to dive back into the - current run, or do nothing, accordingly. - - Arguments required: eol - ''' - - options = ['?','help','i','m','g','s','db','ts','min','max','bal','id','pop','dir','l','p','t','cont','load','w','q',''] - - while True: - try: - pause = raw_input('\n\t\033[36m (pause) \033[0;0m') - if pause not in options: raise ValueError() - if pause == '': - if eol == 1: self.fx_karoo_pause(1) # return to pause menu as the GP run is complete - else: break # drop back into the current GP run - - if pause == '?' or pause == 'help': - print '\n\t\033[36mSelect one of the following options:\033[0;0m' - print '\t\033[36m\033[1m i \t\033[0;0m Interactive display mode' - print '\t\033[36m\033[1m m \t\033[0;0m Minimal display mode' - print '\t\033[36m\033[1m g \t\033[0;0m Generation display mode' - print '\t\033[36m\033[1m s \t\033[0;0m Silent display mode' - print '\t\033[36m\033[1m db \t\033[0;0m De-Bug display mode' - print '' - print '\t\033[36m\033[1m ts \t\033[0;0m adjust the tournament size' - print '\t\033[36m\033[1m min \t\033[0;0m adjust the minimum number of nodes' - # print '\t\033[36m\033[1m max \t\033[0;0m adjust the maximum Tree depth' - print '\t\033[36m\033[1m bal \t\033[0;0m adjust the balance of genetic operators' - print '' - print '\t\033[36m\033[1m l \t\033[0;0m list Trees with leading fitness scores' - print '\t\033[36m\033[1m t \t\033[0;0m evaluate a single Tree against the test data' - print '' - print '\t\033[36m\033[1m p \t\033[0;0m print a single Tree to screen' - print '\t\033[36m\033[1m id \t\033[0;0m display the current generation ID' - print '\t\033[36m\033[1m pop \t\033[0;0m list all Trees in current population' - print '\t\033[36m\033[1m dir \t\033[0;0m display current working directory' - print '' - print '\t\033[36m\033[1m cont \t\033[0;0m continue evolution, starting with the current population' - print '\t\033[36m\033[1m load \t\033[0;0m load population_s (seed) to replace population_a (current)' - print '\t\033[36m\033[1m w \t\033[0;0m write the evolving population_b to disk' - print '\t\033[36m\033[1m q \t\033[0;0m quit Karoo GP without saving population_b' - print '' - - if eol == 1: print '\t\033[0;0m Remember to archive your final population before your next run!' - else: print '\t\033[36m\033[1m ENTER\033[0;0m to continue ...' - - elif pause == 'i': self.display = 'i'; print '\t Interactive display mode engaged (for control freaks)' - elif pause == 'm': self.display = 'm'; print '\t Minimal display mode engaged (for recovering control freaks)' - elif pause == 'g': self.display = 'g'; print '\t Generation display mode engaged (for GP gurus)' - elif pause == 's': self.display = 's'; print '\t Silent display mode engaged (for zen masters)' - elif pause == 'db': self.display = 'db'; print '\t De-Bug display mode engaged (for vouyers)' - - - elif pause == 'ts': # adjust the tournament size - menu = range(2,self.tree_pop_max + 1) # set to total population size only for the sake of experimentation - while True: - try: - print '\n\t The current tournament size is:', self.tourn_size - query = int(raw_input('\t Adjust the tournament size (suggest 10): ')) - if query not in menu: raise ValueError() - self.tourn_size = query; break - except ValueError: print '\n\t\033[32m Enter a number from 2 including', str(self.tree_pop_max) + ".", 'Try again ...\033[0;0m' - - - elif pause == 'min': # adjust the global, minimum number of nodes per Tree - menu = range(3,1001) # we must have at least 3 nodes, as in: x * y; 1000 is an arbitrary number - while True: - try: - print '\n\t The current minimum number of nodes is:', self.tree_depth_min - query = int(raw_input('\t Adjust the minimum number of nodes for all Trees (min 3): ')) - if query not in menu: raise ValueError() - self.tree_depth_min = query; break - except ValueError: print '\n\t\033[32m Enter a number from 3 including 1000. Try again ...\033[0;0m' - - - # NEED TO ADD: adjustable tree_depth_max - #elif pause == 'max': # adjust the global, adjusted maximum Tree depth - # - # menu = range(1,11) - # while True: - # try: - # print '\n\t The current \033[3madjusted\033[0;0m maximum Tree depth is:', self.tree_depth_max - # query = int(raw_input('\n\t Adjust the global maximum Tree depth to (1 ... 10): ')) - # if query not in menu: raise ValueError() - # if query < self.tree_depth_max: - # print '\n\t\033[32m This value is less than the current value.\033[0;0m' - # conf = raw_input('\n\t Are you ok with this? (y/n) ') - # if conf == 'n': break - # except ValueError: print '\n\t\033[32m Enter a number from 1 including 10. Try again ...\033[0;0m' - - - elif pause == 'bal': # adjust the balance of genetic operators' - print '\n\t The current balance of genetic operators is:' - print '\t\t Reproduction:', self.evolve_repro; tmp_repro = self.evolve_repro - print '\t\t Point Mutation:', self.evolve_point; tmp_point = self.evolve_point - print '\t\t Branch Mutation:', self.evolve_branch; tmp_branch = self.evolve_branch - print '\t\t Crossover:', self.evolve_cross, '\n'; tmp_cross = self.evolve_cross - - menu = range(0,1000) # 0 to 1000 expresssed as an integer - - while True: - try: - query = raw_input('\t Enter quantity of Trees to be generated by Reproduction: ') - if query not in str(menu): raise ValueError() - elif query == '': break - tmp_repro = int(float(query)); break - except ValueError: print '\n\t\033[32m Enter a number from 0 including 1000. Try again ...\033[0;0m' - - while True: - try: - query = raw_input('\t Enter quantity of Trees to be generated by Point Mutation: ') - if query not in str(menu): raise ValueError() - elif query == '': break - tmp_point = int(float(query)); break - except ValueError: print '\n\t\033[32m Enter a number from 0 including 1000. Try again ...\033[0;0m' - - while True: - try: - query = raw_input('\t Enter quantity of Trees to be generated by Branch Mutation: ') - if query not in str(menu): raise ValueError() - elif query == '': break - tmp_branch = int(float(query)); break - except ValueError: print '\n\t\033[32m Enter a number from 0 including 1000. Try again ...\033[0;0m' - - while True: - try: - query = raw_input('\t Enter quantity of Trees to be generated by Crossover: ') - if query not in str(menu): raise ValueError() - elif query == '': break - tmp_cross = int(float(query)); break - except ValueError: print '\n\t\033[32m Enter a number from 0 including 1000. Try again ...\033[0;0m' - - if tmp_repro + tmp_point + tmp_branch + tmp_cross != self.tree_pop_max: print '\n\t The sum of the above does not equal', self.tree_pop_max, 'Try again ...' - else: - print '\n\t The revised balance of genetic operators is:' - self.evolve_repro = tmp_repro; print '\t\t Reproduction:', self.evolve_repro - self.evolve_point = tmp_point; print '\t\t Point Mutation:', self.evolve_point - self.evolve_branch = tmp_branch; print '\t\t Branch Mutation:', self.evolve_branch - self.evolve_cross = tmp_cross; print '\t\t Crossover:', self.evolve_cross - - - elif pause == 'l': # display dictionary of Trees with the best fitness score - print '\n\t The leading Trees and their associated expressions are:' - for n in sorted(self.fittest_dict): print '\t ', n, ':', self.fittest_dict[n] - - - elif pause == 't': # evaluate a Tree against the TEST data - if self.generation_id > 1: - menu = range(1, len(self.population_b)) - while True: - try: - query = raw_input('\n\t Select a Tree in population_b to test: ') - if query not in str(menu) or query == '0': raise ValueError() - elif query == '': break - - self.fx_eval_poly(self.population_b[int(query)]) # generate the raw and sympified equation for the given Tree using SymPy - - # get simplified expression and process it by TF - tested 2017 02/02 - expr = str(self.algo_sym) # might change this to algo_raw for more correct expression evaluation - result = self.fx_fitness_eval(expr, self.data_test, get_labels=True) - - print '\n\t\033[36mTree', query, 'yields (raw):', self.algo_raw, '\033[0;0m' - print '\t\033[36mTree', query, 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m\n' - - # test user selected Trees using TF - tested 2017 02/02 - if self.kernel == 'c': self.fx_fitness_test_classify(result); break - elif self.kernel == 'r': self.fx_fitness_test_regress(result); break - elif self.kernel == 'm': self.fx_fitness_test_match(result); break - # elif self.kernel == '[other]': self.fx_fitness_test_[other](result); break - - except ValueError: print '\n\t\033[32m Enter a number from 1 including', str(len(self.population_b) - 1) + ".", 'Try again ...\033[0;0m' - - else: print '\n\t\033[32m Karoo GP does not enable evaluation of the foundation population. Be patient ...\033[0;0m' - - - elif pause == 'p': # print a Tree to screen -- NEED TO ADD: SymPy graphical print option - if self.generation_id == 1: - menu = range(1,len(self.population_a)) - while True: - try: - query = raw_input('\n\t Select a Tree to print: ') - if query not in str(menu) or query == '0': raise ValueError() - elif query == '': break - self.fx_display_tree(self.population_a[int(query)]); break - except ValueError: print '\n\t\033[32m Enter a number from 1 including', str(len(self.population_a) - 1) + ".", 'Try again ...\033[0;0m' - - elif self.generation_id > 1: - menu = range(1,len(self.population_b)) - while True: - try: - query = raw_input('\n\t Select a Tree to print: ') - if query not in str(menu) or query == '0': raise ValueError() - elif query == '': break - self.fx_display_tree(self.population_b[int(query)]); break - except ValueError: print '\n\t\033[32m Enter a number from 1 including', str(len(self.population_b) - 1) + ".", 'Try again ...\033[0;0m' - - else: print '\n\t\033[36m There is nor forest for which to see the Trees.\033[0;0m' - - - elif pause == 'id': print '\n\t The current generation is:', self.generation_id - - - elif pause == 'pop': # list Trees in the current population - print '' - if self.generation_id == 1: - for tree_id in range(1, len(self.population_a)): - self.fx_eval_poly(self.population_a[tree_id]) # extract the expression - print '\t\033[36m Tree', self.population_a[tree_id][0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' - - elif self.generation_id > 1: - for tree_id in range(1, len(self.population_b)): - self.fx_eval_poly(self.population_b[tree_id]) # extract the expression - print '\t\033[36m Tree', self.population_b[tree_id][0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' - - else: print '\n\t\033[36m There is nor forest for which to see the Trees.\033[0;0m' - - - elif pause == 'dir': print '\n\t The current working directory is:', self.path - - - elif pause == 'cont': # continue evolution, starting with the current population - menu = range(1,101) - while True: - try: - query = raw_input('\n\t How many more generations would you like to add? (1-100): ') - if query not in str(menu) or query == '0': raise ValueError() - elif query == '': break - self.generation_max = self.generation_max + int(query) - next_gen_start = self.generation_id + 1 - self.fx_karoo_continue(next_gen_start) # continue evolving, starting with the last population - except ValueError: print '\n\t\033[32m Enter a number from 1 including 100. Try again ...\033[0;0m' - - - elif pause == 'load': # load population_s to replace population_a - while True: - try: - query = raw_input('\n\t Overwrite the current population with population_s? ') - if query not in ['y','n']: raise ValueError() - if query == 'y': self.fx_karoo_data_recover(self.filename['s']); break - elif query == 'n': break - except ValueError: print '\n\t\033[32m Enter (y)es or (n)o. Try again ...\033[0;0m' - - - elif pause == 'w': # write the evolving population_b to disk - if self.generation_id > 1: - self.fx_archive_tree_write(self.population_b, 'b') - print '\t\033[36m All current members of the evolving population_b saved to .csv\033[0;0m' - - else: print '\n\t\033[36m The evolving population_b does not yet exist\033[0;0m' - - - elif pause == 'q': - if eol == 0: # if the GP run is not at the final generation - query = raw_input('\n\t \033[32mThe current population_b will be lost!\033[0;0m\n\n\t Are you certain you want to quit? (y/n)') - if query == 'y': - self.fx_archive_params_write('Desktop') # save run-time parameters to disk - sys.exit() # quit the script without saving population_b - else: break - - else: # if the GP run is complete - query = raw_input('\n\t Are you certain you want to quit? (y/n)') - if query == 'y': - print '\n\t \033[32mYour Trees and runtime parameters are archived in karoo_gp/runs/\033[0;0m' - self.fx_archive_params_write('Desktop') # save run-time parameters to disk - sys.exit() - else: self.fx_karoo_pause(1) - - except ValueError: print '\t\033[32m Select from the options given. Try again ...\033[0;0m' - except KeyboardInterrupt: print '\n\t\033[32m Enter q to quit\033[0;0m' - - return - - - def fx_karoo_continue(self, next_gen_start): - - ''' - This method enables the launch of another full run of Karoo GP, but starting with a seed generation - instead of with a randomly generated first population. This can be used at the end of a standard run to - continue the evoluationary process, or after having recovered a set of trees from a prior run. - - Arguments required: next_gen_start - ''' - - for self.generation_id in range(next_gen_start, self.generation_max + 1): # evolve additional generations of Trees - - print '\n Evolve a population of Trees for Generation', self.generation_id, '...' - self.population_b = ['Karoo GP by Kai Staats, Evolving Generation'] # initialise population_b to host the next generation - - self.fx_fitness_gene_pool() # generate the viable gene pool (compares against gp.tree_depth_min) - self.fx_karoo_reproduce() # method 1 - Reproduction - self.fx_karoo_point_mutate() # method 2 - Point Mutation - self.fx_karoo_branch_mutate() # method 3 - Branch Mutation - self.fx_karoo_crossover() # method 4 - Crossover - self.fx_eval_generation() # evaluate all Trees in a single generation - - self.population_a = self.fx_evolve_pop_copy(self.population_b, ['GP Tree by Kai Staats, Generation ' + str(self.generation_id)]) - - # "End of line, man!" --CLU - target = open(self.filename['f'], 'w') # reset the .csv file for the final population - target.close() - - self.fx_archive_tree_write(self.population_b, 'f') # save the final generation of Trees to disk - self.fx_karoo_eol() - - return - - - def fx_karoo_eol(self): - - ''' - The very last method to run in Karoo GP. - - Arguments required: none - ''' - - print '\n\033[3m "It is not the strongest of the species that survive, nor the most intelligent,\033[0;0m' - print '\033[3m but the one most responsive to change."\033[0;0m --Charles Darwin' - print '' - print '\033[3m Congrats!\033[0;0m Your multi-generational Karoo GP run is complete.\n' - print '\033[36m Type \033[1m?\033[0;0m\033[36m to review your options or \033[1mq\033[0;0m\033[36m to quit.\033[0;0m\n' - self.fx_karoo_pause(1) - - return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Generate a new Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_gen_tree_initialise(self, TREE_ID, tree_type, tree_depth_base): - - ''' - Assign 13 global variables to the array 'tree'. - - Build the array 'tree' with 13 rows and initally, just 1 column of labels. This array will grow as each new - node is appended. The values of this array are stored as string characters. Numbers will be forced to integers - at the point of execution. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: TREE_ID, tree_type, tree_depth_base - ''' - - self.pop_TREE_ID = TREE_ID # pos 0: a unique identifier for each tree - self.pop_tree_type = tree_type # pos 1: a global constant based upon the initial user setting - self.pop_tree_depth_base = tree_depth_base # pos 2: a global variable which conveys 'tree_depth_base' as unique to each new Tree - self.pop_NODE_ID = 1 # pos 3: unique identifier for each node; this is the INDEX KEY to this array - self.pop_node_depth = 0 # pos 4: depth of each node when committed to the array - self.pop_node_type = '' # pos 5: root, function, or terminal - self.pop_node_label = '' # pos 6: operator [+, -, *, ...] or terminal [a, b, c, ...] - self.pop_node_parent = '' # pos 7: parent node - self.pop_node_arity = '' # pos 8: number of nodes attached to each non-terminal node - self.pop_node_c1 = '' # pos 9: child node 1 - self.pop_node_c2 = '' # pos 10: child node 2 - self.pop_node_c3 = '' # pos 11: child node 3 (assumed max of 3 with boolean operator 'if') - self.pop_fitness = '' # pos 12: fitness score following Tree evaluation - - self.tree = np.array([ ['TREE_ID'],['tree_type'],['tree_depth_base'],['NODE_ID'],['node_depth'],['node_type'],['node_label'],['node_parent'],['node_arity'],['node_c1'],['node_c2'],['node_c3'],['fitness'] ]) - - return - - - ### Root Node ### - - def fx_gen_root_node_build(self): - - ''' - Build the Root node for the initial population. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: none - ''' - - self.fx_gen_function_select() # select the operator for root - - if self.pop_node_arity == 1: # 1 child - self.pop_node_c1 = 2 - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - elif self.pop_node_arity == 2: # 2 children - self.pop_node_c1 = 2 - self.pop_node_c2 = 3 - self.pop_node_c3 = '' - - elif self.pop_node_arity == 3: # 3 children - self.pop_node_c1 = 2 - self.pop_node_c2 = 3 - self.pop_node_c3 = 4 - - else: print '\n\t\033[31m ERROR! In fx_gen_root_node_build: pop_node_arity =', self.pop_node_arity, '\033[0;0m'; self.fx_karoo_pause(0) - - self.pop_node_type = 'root' - - self.fx_gen_node_commit() - - return - - - ### Function Nodes ### - - def fx_gen_function_node_build(self): - - ''' - Build the Function nodes for the intial population. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: none - ''' - - for i in range(1, self.pop_tree_depth_base): # increment depth, from 1 through 'tree_depth_base' - 1 - - self.pop_node_depth = i # increment 'node_depth' - - parent_arity_sum = 0 - prior_sibling_arity = 0 # reset for 'c_buffer' in 'children_link' - prior_siblings = 0 # reset for 'c_buffer' in 'children_link' - - for j in range(1, len(self.tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth - parent_arity_sum = parent_arity_sum + int(self.tree[8][j]) # sum arities of all parent nodes at the prior depth - - # (do *not* merge these 2 "j" loops or it gets all kinds of messed up) - - for j in range(1, len(self.tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth - - for k in range(1, int(self.tree[8][j]) + 1): # increment through each degree of arity for each parent node - self.pop_node_parent = int(self.tree[3][j]) # set the parent 'NODE_ID' ... - prior_sibling_arity = self.fx_gen_function_gen(parent_arity_sum, prior_sibling_arity, prior_siblings) # ... generate a Function ndoe - prior_siblings = prior_siblings + 1 # sum sibling nodes (current depth) who will spawn their own children (cousins? :) - - return - - - def fx_gen_function_gen(self, parent_arity_sum, prior_sibling_arity, prior_siblings): - - ''' - Generate a single Function node for the initial population. - - This method is called by 'fx_gen_function_node_build'. - - Arguments required: parent_arity_sum, prior_sibling_arity, prior_siblings - ''' - - if self.pop_tree_type == 'f': # user defined as (f)ull - self.fx_gen_function_select() # retrieve a function - self.fx_gen_child_link(parent_arity_sum, prior_sibling_arity, prior_siblings) # establish links to children - - elif self.pop_tree_type == 'g': # user defined as (g)row - rnd = np.random.randint(2) - - if rnd == 0: # randomly selected as Function - self.fx_gen_function_select() # retrieve a function - self.fx_gen_child_link(parent_arity_sum, prior_sibling_arity, prior_siblings) # establish links to children - - elif rnd == 1: # randomly selected as Terminal - self.fx_gen_terminal_select() # retrieve a terminal - self.pop_node_c1 = '' - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - self.fx_gen_node_commit() # commit new node to array - prior_sibling_arity = prior_sibling_arity + self.pop_node_arity # sum the arity of prior siblings - - return prior_sibling_arity - - - def fx_gen_function_select(self): - - ''' - Define a single Function (operator extracted from the associated functions.csv) for the initial population. - - This method is called by 'fx_gen_function_gen' and 'fx_gen_root_node_build'. - - Arguments required: none - ''' - - self.pop_node_type = 'func' - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - self.pop_node_label = self.functions[rnd][0] - self.pop_node_arity = int(self.functions[rnd][1]) - - return - - - ### Terminal Nodes ### - - def fx_gen_terminal_node_build(self): - - ''' - Build the Terminal nodes for the intial population. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: none - ''' - - self.pop_node_depth = self.pop_tree_depth_base # set the final node_depth (same as 'gp.pop_node_depth' + 1) - - for j in range(1, len(self.tree[3]) ): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth - - for k in range(1,(int(self.tree[8][j]) + 1)): # increment through each degree of arity for each parent node - self.pop_node_parent = int(self.tree[3][j]) # set the parent 'NODE_ID' ... - self.fx_gen_terminal_gen() # ... generate a Terminal node - - return - - - def fx_gen_terminal_gen(self): - - ''' - Generate a single Terminal node for the initial population. - - This method is called by 'fx_gen_terminal_node_build'. - - Arguments required: none - ''' - - self.fx_gen_terminal_select() # retrieve a terminal - self.pop_node_c1 = '' - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - self.fx_gen_node_commit() # commit new node to array - - return - - - def fx_gen_terminal_select(self): - - ''' - Define a single Terminal (variable extracted from the top row of the associated TRAINING data) - - This method is called by 'fx_gen_terminal_gen' and 'fx_gen_function_gen'. - - Arguments required: none - ''' - - self.pop_node_type = 'term' - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - self.pop_node_label = self.terminals[rnd] - self.pop_node_arity = 0 - - return - - - ### The Lovely Children ### - - def fx_gen_child_link(self, parent_arity_sum, prior_sibling_arity, prior_siblings): - - ''' - Link each parent node to its children in the intial population. - - This method is called by 'fx_gen_function_gen'. - - Arguments required: parent_arity_sum, prior_sibling_arity, prior_siblings - ''' - - c_buffer = 0 - - for n in range(1, len(self.tree[3]) ): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][n]) == self.pop_node_depth - 1: # find all nodes that reside at the prior (parent) 'node_depth' - - c_buffer = self.pop_NODE_ID + (parent_arity_sum + prior_sibling_arity - prior_siblings) # One algo to rule the world! - - if self.pop_node_arity == 0: # terminal in a Grow Tree - self.pop_node_c1 = '' - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - elif self.pop_node_arity == 1: # 1 child - self.pop_node_c1 = c_buffer - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - elif self.pop_node_arity == 2: # 2 children - self.pop_node_c1 = c_buffer - self.pop_node_c2 = c_buffer + 1 - self.pop_node_c3 = '' - - elif self.pop_node_arity == 3: # 3 children - self.pop_node_c1 = c_buffer - self.pop_node_c2 = c_buffer + 1 - self.pop_node_c3 = c_buffer + 2 - - else: print '\n\t\033[31m ERROR! In fx_gen_child_link: pop_node_arity =', self.pop_node_arity, '\033[0;0m'; self.fx_karoo_pause(0) - - return - - - def fx_gen_node_commit(self): - - ''' - Commit the values of a new node (root, function, or terminal) to the array 'tree'. - - This method is called by 'fx_gen_root_node_build' and 'fx_gen_function_gen' and 'fx_gen_terminal_gen'. - - Arguments required: none - ''' - - self.tree = np.append(self.tree, [ [self.pop_TREE_ID],[self.pop_tree_type],[self.pop_tree_depth_base],[self.pop_NODE_ID],[self.pop_node_depth],[self.pop_node_type],[self.pop_node_label],[self.pop_node_parent],[self.pop_node_arity],[self.pop_node_c1],[self.pop_node_c2],[self.pop_node_c3],[self.pop_fitness] ], 1) - - self.pop_NODE_ID = self.pop_NODE_ID + 1 - - return - - - def fx_gen_tree_build(self, TREE_ID, tree_type, tree_depth_base): - - ''' - This method combines 4 sub-methods into a single method for ease of deployment. It is designed to executed - within a loop such that an entire population is built. However, it may also be run from the command line, - passing a single TREE_ID to the method. - - 'tree_type' is either (f)ull or (g)row. Note, however, that when the user selects 'ramped 50/50' at launch, - it is still (f) or (g) which are passed to this method. - - This method is called by a 'fx_evolve_crossover' and 'fx_evolve_grow_mutate' and 'fx_karoo_construct'. - - Arguments required: TREE_ID, tree_type, tree_depth_base - ''' - - self.fx_gen_tree_initialise(TREE_ID, tree_type, tree_depth_base) # initialise a new Tree - self.fx_gen_root_node_build() # build the Root node - self.fx_gen_function_node_build() # build the Function nodes - self.fx_gen_terminal_node_build() # build the Terminal nodes - - return # each Tree is written to 'gp.tree' - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Evaluate a Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_eval_poly(self, tree): - - ''' - Evaluate a Tree and generate its multivariate expression (both raw and Sympified). - - We need to extract the variables from the expression. However, these variables are no longer correlated - to the original variables listed across the top of each column of data.csv. Therefore, we must re-assign - the respective values for each subsequent row in the data .csv, for each Tree's unique expression. - - Arguments required: tree - ''' - - self.algo_raw = self.fx_eval_label(tree, 1) # pass the root 'node_id', then flatten the Tree to a string - self.algo_sym = sympify(self.algo_raw) # convert string to a functional expression (the coolest line in Karoo! :) - - return - - - def fx_eval_label(self, tree, node_id): - - ''' - Evaluate all or part of a Tree (starting at node_id) and return a raw mutivariate expression ('algo_raw'). - - In the main code, this method is called once per Tree, but may be called at any time to prepare an expression - for any full or partial (branch) Tree contained in 'population'. - - Pass the starting node for recursion via the local variable 'node_id' where the local variable 'tree' is a - copy of the Tree you desire to evaluate. - - Arguments required: tree, node_id - ''' - - # if tree[6, node_id] == 'not': tree[6, node_id] = ', not' # temp until this can be fixed at data_load - - node_id = int(node_id) - - if tree[8, node_id] == '0': # arity of 0 for the pattern '[term]' - return '(' + tree[6, node_id] + ')' # 'node_label' (function or terminal) - - else: - if tree[8, node_id] == '1': # arity of 1 for the explicit pattern 'not [term]' - return self.fx_eval_label(tree, tree[9, node_id]) + tree[6, node_id] # original code - - elif tree[8, node_id] == '2': # arity of 2 for the pattern '[func] [term] [func]' - return self.fx_eval_label(tree, tree[9, node_id]) + tree[6, node_id] + self.fx_eval_label(tree, tree[10, node_id]) - - elif tree[8, node_id] == '3': # arity of 3 for the explicit pattern 'if [term] then [term] else [term]' - return tree[6, node_id] + self.fx_eval_label(tree, tree[9, node_id]) + ' then ' + self.fx_eval_label(tree, tree[10, node_id]) + ' else ' + self.fx_eval_label(tree, tree[11, node_id]) - - - def fx_eval_id(self, tree, node_id): - - ''' - Evaluate all or part of a Tree and return a list of all 'NODE_ID's. - - This method generates a list of all 'NODE_ID's from the given Node and below. It is used primarily to generate - 'branch' for the multi-generational mutation of Trees. - - Pass the starting node for recursion via the local variable 'node_id' where the local variable 'tree' is a copy - of the Tree you desire to evaluate. - - Arguments required: tree, node_id - ''' - - node_id = int(node_id) - - if tree[8, node_id] == '0': # arity of 0 for the pattern '[NODE_ID]' - return tree[3, node_id] # 'NODE_ID' - - else: - if tree[8, node_id] == '1': # arity of 1 for the pattern '[NODE_ID], [NODE_ID]' - return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) - - elif tree[8, node_id] == '2': # arity of 2 for the pattern '[NODE_ID], [NODE_ID], [NODE_ID]' - return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) + ', ' + self.fx_eval_id(tree, tree[10, node_id]) - - elif tree[8, node_id] == '3': # arity of 3 for the pattern '[NODE_ID], [NODE_ID], [NODE_ID], [NODE_ID]' - return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) + ', ' + self.fx_eval_id(tree, tree[10, node_id]) + ', ' + self.fx_eval_id(tree, tree[11, node_id]) - - - def fx_eval_generation(self): - - ''' - This method invokes the evaluation of an entire generation of Trees, as engaged by karoo_gp_server.py and the - 'cont' function of karoo_go_main.py. It automatically evaluates population_b before invoking the copy of _b to _a. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print '\n Evaluate all Trees in Generation', self.generation_id - if self.display == 'i': self.fx_karoo_pause(0) - - self.fx_evolve_tree_renum(self.population_b) # population renumber - self.fx_fitness_gym(self.population_b) # run 'fx_eval', 'fx_fitness', 'fx_fitness_store', and fitness record - self.fx_archive_tree_write(self.population_b, 'a') # archive current population as foundation for next generation - - if self.display != 's': - print '\n Copy gp.population_b to gp.population_a\n' - - return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Train and Test a Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_fitness_gym(self, population): - - ''' - Part 1 evaluates each expression against the data, line for line. This is the most time consuming and - computationally expensive part of genetic programming. When GPUs are available, the performance can increase - by many orders of magnitude. - - Part 2 evaluates every Tree in each generation to determine which have the best, overall fitness score. This - could be the highest or lowest depending upon if the fitness function is maximising (higher is better) or - minimising (lower is better). The total fitness score is then saved with each Tree in the external .csv file. - - Part 3 compares the fitness of each Tree to the prior best fit in order to track those that improve with each - comparison. For matching functions, all the Trees will have the same fitness score, but they may present more - than one solution. For minimisation and maximisation functions, the final Tree should present the best overall - fitness for that generation. It is important to note that Part 3 does *not* in any way influence the Tournament - Selection which is a stand-alone process. - - Arguments required: population - ''' - - fitness_best = 0 - self.fittest_dict = {} - time_sum = 0 - - for tree_id in range(1, len(population)): - - ### PART 1 - GENERATE MULTIVARIATE EXPRESSION FOR EACH TREE ### - self.fx_eval_poly(population[tree_id]) # extract the expression - if self.display not in ('s'): print '\t\033[36mTree', population[tree_id][0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' - - - ### PART 2 - EVALUATE FITNESS FOR EACH TREE AGAINST TRAINING DATA ### - fitness = 0 - - expr = str(self.algo_sym) # get sympified expression and process it with TF - tested 2017 02/02 - result = self.fx_fitness_eval(expr, self.data_train) - fitness = result['fitness'] # extract fitness score - - if self.display == 'i': - print '\t \033[36m with fitness sum:\033[1m', fitness, '\033[0;0m\n' - - self.fx_fitness_store(population[tree_id], fitness) # store Fitness with each Tree - - - ### PART 3 - COMPARE FITNESS OF ALL TREES IN CURRENT GENERATION ### - if self.kernel == 'c': # display best fit Trees for the CLASSIFY kernel - if fitness >= fitness_best: # find the Tree with Maximum fitness score - fitness_best = fitness # set best fitness score - self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if fitness >= prior - - elif self.kernel == 'r': # display best fit Trees for the REGRESSION kernel - if fitness_best == 0: fitness_best = fitness # set the baseline first time through - if fitness <= fitness_best: # find the Tree with Minimum fitness score - fitness_best = fitness # set best fitness score - self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if fitness <= prior - - elif self.kernel == 'm': # display best fit Trees for the MATCH kernel - if fitness == self.data_train_rows: # find the Tree with a perfect match for all data rows - fitness_best = fitness # set best fitness score - self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if all rows match - - # elif self.kernel == '[other]': # display best fit Trees for the [other] kernel - # if fitness [>=, <=] fitness_best: # find the Tree with [Maximum or Minimum] fitness score - # fitness_best = fitness # set best fitness score - # self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary - - print '\n\033[36m ', len(self.fittest_dict.keys()), 'trees\033[1m', np.sort(self.fittest_dict.keys()), '\033[0;0m\033[36moffer the highest fitness scores.\033[0;0m' - if self.display == 'g': self.fx_karoo_pause(0) - - return - - - def fx_fitness_eval(self, expr, data, get_labels = False): # used to be fx_fitness_eval - - ''' - Computes tree expression using TensorFlow (TF) returning results and fitness scores. - - This method orchestrates most of the TF routines by parsing input string expression and converting it into TF - operation graph which then is processed in an isolated TF session to compute the results and corresponding fitness - values. - - 'self.tf_device' - controls which device will be used for computations (CPU or GPU). - 'self.tf_device_log' - controls device placement logging (debug only). - - Args: - 'expr' - a string containing math expression to be computed on the data. Variable names should match corresponding - terminal names in 'self.terminals'. Only algebraic operations are currently supported (+, -, *, /, **). - - 'data' - an 'n by m' matrix of the data points containing n observations and m features each. Variable order should - match corresponding order of terminals in 'self.terminals'. - - 'get_labels' - a boolean flag which controls whether classification labels should be extracted from the results. - This is applied only to the CLASSIFY kernel and defaults to 'False'. - - Returns: - A dict mapping keys to the following outputs: - 'result' - an array of the results of applying given expression to the data - 'labels' - an array of the labels extracted from the results; defined only for CLASSIFY kernel, None otherwise - 'solution' - an array of the solution values extracted from the data (variable 's' in the dataset) - 'pairwise_fitness' - an array of the element-wise results of applying corresponding fitness kernel function - 'fitness' - aggregated scalar fitness score - - Arguments required: expr, data - ''' - - # Initialize TensorFlow session - tf.reset_default_graph() # Reset TF internal state and cache (after previous processing) - config = tf.ConfigProto(log_device_placement=self.tf_device_log, allow_soft_placement=True) - config.gpu_options.allow_growth = True - - with tf.Session(config=config) as sess: - with sess.graph.device(self.tf_device): - - # Load data into TF - tensors = {} - for i in range(len(self.terminals)): - var = self.terminals[i] - tensors[var] = tf.constant(data[:, i], dtype=tf.float32) - - # Transform string expression into TF operation graph - result = self.fx_fitness_expr_parse(expr, tensors) - - labels = tf.no_op() # a placeholder, applies only to CLASSIFY kernel - solution = tensors['s'] # solution value is assumed to be stored in 's' terminal - - # Add fitness computation into TF graph - if self.kernel == 'c': # CLASSIFY kernels - if get_labels: labels = tf.map_fn(self.fx_fitness_labels_map, result, dtype=[tf.int32, tf.string], swap_memory=True) - pairwise_fitness = self.fx_fitness_train_classify(result, tf.cast(solution, tf.float32)) - - elif self.kernel == 'r': # REGRESSION kernel - pairwise_fitness = self.fx_fitness_train_regress(result, tf.cast(solution, tf.float32)) - - elif self.kernel == 'm': # MATCH kernel - pairwise_fitness = self.fx_fitness_train_match(result, solution) - - # elif self.kernel == '[other]': # [OTHER] kernel - # pairwise_fitness = self.fx_fitness_train_[other](result ?, solution ?) - - else: raise Exception('Kernel type is wrong or missing. You entered {}'.format(self.kernel)) - - fitness = tf.reduce_sum(pairwise_fitness) - - # Process TF graph and collect the results - result, labels, solution, fitness, pairwise_fitness = sess.run([result, labels, solution, fitness, pairwise_fitness]) - - return {'result': result, 'labels': labels, 'solution': solution, 'fitness': float(fitness), 'pairwise_fitness': pairwise_fitness} - - - def fx_fitness_expr_parse(self, expr, tensors): - - ''' - Extract expression tree from the string algo_sym and transform into TensorFlow (TF) graph. - - Arguments required: expr, tensors - ''' - - tree = ast.parse(expr, mode='eval').body - - return self.fx_fitness_node_parse(tree, tensors) - - - def fx_fitness_node_parse(self, node, tensors): - - ''' - Recursively transforms parsed expression tree into TensorFlow (TF) graph. - - Arguments required: node, tensors - ''' - - if isinstance(node, ast.Name): # - return tensors[node.id] - - elif isinstance(node, ast.Num): # - shape = tensors[tensors.keys()[0]].get_shape() - return tf.constant(node.n, shape=shape, dtype=tf.float32) - - elif isinstance(node, ast.BinOp): # - return operators[type(node.op)](self.fx_fitness_node_parse(node.left, tensors), self.fx_fitness_node_parse(node.right, tensors)) - - elif isinstance(node, ast.UnaryOp): # e.g., -1 - return operators[type(node.op)](self.fx_fitness_node_parse(node.operand, tensors)) - - else: raise TypeError(node) - - - def fx_fitness_labels_map(self, result): - - ''' - Creates label extraction TensorFlow (TF) sub-graph for CLASSIFY kernel defined as a sequence of boolean conditions. - Outputs an array of tuples containing label extracted from the result and corresponding boolean condition triggered. - - The original (pre-TensorFlow) code is as follows: - - skew = (self.class_labels / 2) - 1 # '-1' keeps a binary classification splitting over the origin - if solution == 0 and result <= 0 - skew; fitness = 1: # check for first class (the left-most bin) - elif solution == self.class_labels - 1 and result > solution - 1 - skew; fitness = 1: # check for last class (the right-most bin) - elif solution - 1 - skew < result <= solution - skew; fitness = 1: # check for class bins between first and last - else: fitness = 0 # no class match - - See 'fx_fitness_train_classify' for a description of the multi-class classifier. - - Arguments required: result - ''' - - skew = (self.class_labels / 2) - 1 - label_rules = {self.class_labels - 1: (tf.constant(self.class_labels - 1), tf.constant(' > {}'.format(self.class_labels - 2 - skew)))} - - for class_label in range(self.class_labels - 2, 0, -1): - cond = (class_label - 1 - skew < result) & (result <= class_label - skew) - label_rules[class_label] = tf.cond(cond, lambda: (tf.constant(class_label), tf.constant(' <= {}'.format(class_label - skew))), lambda: label_rules[class_label + 1]) - - zero_rule = tf.cond(result <= 0 - skew, lambda: (tf.constant(0), tf.constant(' <= {}'.format(0 - skew))), lambda: label_rules[1]) - - return zero_rule - - - def fx_fitness_train_classify(self, result, solution): # CLASSIFICATION kernel - - ''' - Creates element-wise fitness computation TensorFlow (TF) sub-graph for CLASSIFY kernel. - - This method uses the 'sympified' (SymPy) expression ('algo_sym') created in 'fx_eval_poly' and the data set - loaded at run-time to evaluate the fitness of the selected kernel. - - This multiclass classifer compares each row of a given Tree to the known solution, comparing estimated values - (labels) generated by Karoo GP against the correct labels. This method is able to work with any number of class - labels, from 2 to n. The left-most bin includes -inf. The right-most bin includes +inf. Those inbetween are - by default confined to the spacing of 1.0 each, as defined by: - - (solution - 1) < result <= solution - - The skew adjusts the boundaries of the bins such that they fall on both the negative and positive sides of the - origin. At the time of this writing, an odd number of class labels will generate an extra bin on the positive - side of origin as it has not yet been determined the effect of enabling the middle bin to include both a - negative and positive space. - - Arguments required: result, solution - ''' - - skew = (self.class_labels / 2) - 1 - rule11 = tf.equal(solution, 0) - rule12 = tf.less_equal(result, 0 - skew) - rule13 = tf.logical_and(rule11, rule12) - rule21 = tf.equal(solution, self.class_labels - 1) - rule22 = tf.greater(result, solution - 1 - skew) - rule23 = tf.logical_and(rule21, rule22) - rule31 = tf.less(solution - 1 - skew, result) - rule32 = tf.less_equal(result, solution - skew) - rule33 = tf.logical_and(rule31, rule32) - - return tf.cast(tf.logical_or(tf.logical_or(rule13, rule23), rule33), tf.int32) - - - def fx_fitness_train_regress(self, result, solution): # REGRESSION kernel - - ''' - Creates element-wise fitness computation TensorFlow (TF) sub-graph for REGRESSION kernel. - - This is a minimisation function which seeks a result which is closest to the solution. - - [need to write more] - - Arguments required: result, solution - ''' - - return tf.abs(solution - result) - - - def fx_fitness_train_match(self, result, solution): # MATCH kernel - - ''' - Creates element-wise fitness computation TensorFlow (TF) sub-graph for MATCH kernel. - - This is a maximization function which seeks an exact solution (a perfect match). - - [need to write more] - - Arguments required: result, solution - ''' - - return tf.cast(tf.equal(solution, result), tf.int32) - - - # def fx_fitness_train_[other](self, result, solution): # [OTHER] kernel - - # ''' - # Creates element-wise fitness computation TensorFlow (TF) sub-graph for [other] kernel. - - # This is a [minimisation or maximization] function which [insert description]. - - # return tf.[?]([insert formula]) - # ''' - - - def fx_fitness_store(self, tree, fitness): - - ''' - Records the fitness and length of the raw algorithm (multivariate expression) to the Numpy array. Parsimony can - be used to apply pressure to the evolutionary process to select from a set of trees with the same fitness function - the one(s) with the simplest (shortest) multivariate expression. - - Arguments required: tree, fitness - ''' - - fitness = float(fitness) - fitness = round(fitness, self.precision) - - tree[12][1] = fitness # store the fitness with each tree - tree[12][2] = len(str(self.algo_raw)) # store the length of the raw algo for parsimony - # if len(tree[3]) > 4: # if the Tree array is wide enough -- SEE SCRATCHPAD - - return - - - def fx_fitness_tournament(self, tourn_size): - - ''' - Select one Tree by means of a Tournament in which 'tourn_size' contenders are randomly selected and then - compared for their respective fitness (as determined in 'fx_fitness_gym'). The tournament is engaged for each - of the four types of inter-generational evolution: reproduction, point mutation, branch (full and grow) - mutation, and crossover (sexual reproduction). - - The original Tournament Selection drew directly from the foundation generation (gp.generation_a). However, - with the introduction of a minimum number of nodes as defined by the user ('gp.tree_depth_min'), - 'gp.gene_pool' provides only from those Trees which meet all criteria. - - With upper (max depth) and lower (min nodes) invoked, one may enjoy interesting results. Stronger boundary - parameters (a reduced gap between the min and max number of nodes) may invoke more compact solutions, but also - runs the risk of elitism, even total population die-off where a healthy population once existed. - - Arguments required: tourn_size - ''' - - tourn_test = 0 - # short_test = 0 # an incomplete parsimony test (seeking shortest solution) - - if self.display == 'i': print '\n\tEnter the tournament ...' - - for n in range(tourn_size): - # tree_id = np.random.randint(1, self.tree_pop_max + 1) # former method of selection from the unfiltered population - rnd = np.random.randint(len(self.gene_pool)) # select one Tree at random from the gene pool - tree_id = int(self.gene_pool[rnd]) - - fitness = float(self.population_a[tree_id][12][1]) # extract the fitness from the array - fitness = round(fitness, self.precision) # force 'result' and 'solution' to the same number of floating points - - if self.fitness_type == 'max': # if the fitness function is Maximising - - # first time through, 'tourn_test' will be initialised below - - if fitness > tourn_test: # if the current Tree's 'fitness' is greater than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '>', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # set 'TREE_ID' for the new leader - tourn_test = fitness # set 'fitness' of the new leader - # short_test = int(self.population_a[tree_id][12][2]) # set len(algo_raw) of new leader - - elif fitness == tourn_test: # if the current Tree's 'fitness' is equal to the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '=', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # in case there is no variance in this tournament - # tourn_test remains unchanged - - # NEED TO ADD: option for parsimony - # if int(self.population_a[tree_id][12][2]) < short_test: - # short_test = int(self.population_a[tree_id][12][2]) # set len(algo_raw) of new leader - # print '\t\033[36m with improved parsimony score of:\033[1m', short_test, '\033[0;0m' - - elif fitness < tourn_test: # if the current Tree's 'fitness' is less than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '<', tourn_test, 'and is ignored\033[0;0m' - # tourn_lead remains unchanged - # tourn_test remains unchanged - - else: print '\n\t\033[31m ERROR! In fx_fitness_tournament: fitness =', fitness, 'and tourn_test =', tourn_test, '\033[0;0m'; self.fx_karoo_pause(0) - - - elif self.fitness_type == 'min': # if the fitness function is Minimising - - if tourn_test == 0: # first time through, 'tourn_test' is given a baseline value - tourn_test = fitness - - if fitness < tourn_test: # if the current Tree's 'fitness' is less than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '<', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # set 'TREE_ID' for the new leader - tourn_test = fitness # set 'fitness' of the new leader - - elif fitness == tourn_test: # if the current Tree's 'fitness' is equal to the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '=', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # in case there is no variance in this tournament - # tourn_test remains unchanged - - elif fitness > tourn_test: # if the current Tree's 'fitness' is greater than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '>', tourn_test, 'and is ignored\033[0;0m' - # tourn_lead remains unchanged - # tourn_test remains unchanged - - else: print '\n\t\033[31m ERROR! In fx_fitness_tournament: fitness =', fitness, 'and tourn_test =', tourn_test, '\033[0;0m'; self.fx_karoo_pause(0) - - - tourn_winner = np.copy(self.population_a[tourn_lead]) # copy full Tree so as to not inadvertantly modify the original tree - - if self.display == 'i': print '\n\t\033[36mThe winner of the tournament is Tree:\033[1m', tourn_winner[0][1], '\033[0;0m' - - return tourn_winner - - - def fx_fitness_gene_pool(self): - - ''' - With the introduction of the minimum number of nodes parameter (gp.tree_depth_min), the means by which the - lower node count is enforced is through the creation of a gene pool from those Trees which contain equal or - greater nodes to the user defined limit. - - When the minimum node count is human guided, it can help keep the solution from defaulting to a local minimum, - as with 't/t' in the Kepler problem. However, the ramification of this limitation on the evolutionary process - has not been fully studied. - - This method is automatically invoked with every Tournament Selection ('fx_fitness_tournament'). - - At this time, the gene pool does *not* limit the number of times any given Tree may be selected for mutation or - reproduction nor does it take into account parsimony (seeking the simplest multivariate expression). - - Arguments required: none - ''' - - self.gene_pool = [] - if self.display == 'i': print '\n Prepare a viable gene pool ...'; self.fx_karoo_pause(0) - - for tree_id in range(1, len(self.population_a)): - - self.fx_eval_poly(self.population_a[tree_id]) # extract the expression - - if len(self.population_a[tree_id][3])-1 >= self.tree_depth_min and self.algo_sym != 1: # check if Tree meets the requirements - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has >=', self.tree_depth_min, 'nodes and is added to the gene pool\033[0;0m' - self.gene_pool.append(self.population_a[tree_id][0][1]) - - if len(self.gene_pool) > 0 and self.display == 'i': print '\n\t The total population of the gene pool is', len(self.gene_pool); self.fx_karoo_pause(0) - - elif len(self.gene_pool) <= 0: # the evolutionary constraints were too tight, killing off the entire population - # self.generation_id = self.generation_id - 1 # revert the increment of the 'generation_id' - # self.generation_max = self.generation_id # catch the unused "cont" values in the 'fx_karoo_pause' method - print "\n\t\033[31m\033[3m 'They're dead Jim. They're all dead!'\033[0;0m There are no Trees in the gene pool. You should archive your populations and (q)uit."; self.fx_karoo_pause(0) - - return - - - def fx_fitness_test_classify(self, result): - - ''' - Print the Precision-Recall and Confusion Matrix for a CLASSIFICATION run against the test data. - - From scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html - Precision (P) = true_pos / true_pos + false_pos - Recall (R) = true_pos / true_pos + false_neg - harmonic mean of Precision and Recall (F1) = 2(P x R) / (P + R) - - From scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html - y_pred = result, the estimated target values (labels) generated by Karoo GP - y_true = solution, the correct target values (labels) associated with the data - - Arguments required: result - ''' - - for i in range(len(result['result'])): - print '\t\033[36m Data row {} predicts class:\033[1m {} ({} label) as {:.2f}{}\033[0;0m'.format(i, int(result['labels'][0][i]), int(result['solution'][i]), result['result'][i], result['labels'][1][i]) - - print '\n Fitness score: {}'.format(result['fitness']) - print '\n Precision-Recall report:\n', skm.classification_report(result['solution'], result['labels'][0]) - print ' Confusion matrix:\n', skm.confusion_matrix(result['solution'], result['labels'][0]) - - return - - - def fx_fitness_test_regress(self, result): - - ''' - Print the Fitness score and Mean Squared Error for a REGRESSION run against the test data. - ''' - - for i in range(len(result['result'])): - print '\t\033[36m Data row {} predicts value:\033[1m {:.2f} ({:.2f} True)\033[0;0m'.format(i, result['result'][i], result[ 'solution'][i]) - - MSE, fitness = skm.mean_squared_error(result['result'], result['solution']), result['fitness'] - print '\n\t Regression fitness score: {}'.format(fitness) - print '\t Mean Squared Error: {}'.format(MSE) - - return - - - def fx_fitness_test_match(self, result): - - ''' - Print the accuracy for a MATCH kernel run against the test data. - ''' - - for i in range(len(result['result'])): - print '\t\033[36m Data row {} predicts value:\033[1m {} ({} label)\033[0;0m'.format(i, int(result['result'][i]), int(result['solution'][i])) - - print '\n\tMatching fitness score: {}'.format(result['fitness']) - - return - - - # def fx_fitness_test_[other](self, result): - - # ''' - # Print the [statistical measure] for a [OTHER] kernel run against the test data. - # ''' - - # for i in range(len(result['result'])): - # print '\t\033[36m Data row {} predicts value:\033[1m {} ({} label)\033[0;0m'.format(i, int(result['result'][i]), int(result['solution'][i])) - - # print '\n\tFitness score: {}'.format(result['fitness']) - - # return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Evolve a Population | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_evolve_point_mutate(self, tree): - - ''' - Mutate a single point in any Tree (Grow or Full). - - Arguments required: tree - ''' - - node = np.random.randint(1, len(tree[3])) # randomly select a point in the Tree (including root) - if self.display == 'i': print '\t\033[36m with', tree[5][node], 'node\033[1m', tree[3][node], '\033[0;0m\033[36mchosen for mutation\n\033[0;0m' - elif self.display == 'db': print '\n\n\033[33m *** Point Mutation *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - if tree[5][node] == 'root': - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - tree[6][node] = self.functions[rnd][0] # replace function (operator) - - elif tree[5][node] == 'func': - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - tree[6][node] = self.functions[rnd][0] # replace function (operator) - - elif tree[5][node] == 'term': - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[6][node] = self.terminals[rnd] # replace terminal (variable) - - else: print '\n\t\033[31m ERROR! In fx_evolve_point_mutate, node_type =', tree[5][node], '\033[0;0m'; self.fx_karoo_pause(0) - - tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data - - if self.display == 'db': print '\n\033[36m This is tourn_winner after node\033[1m', node, '\033[0;0m\033[36mmutation and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - return tree, node # 'node' is returned only to be assigned to the 'tourn_trees' record keeping - - - def fx_evolve_full_mutate(self, tree, branch): - - ''' - Mutate a branch of a Full method Tree. - - The full mutate method is straight-forward. A branch was generated and passed to this method. As the size and - shape of the Tree must remain identical, each node is mutated sequentially (copied from the new Tree to replace - the old, node for node), where functions remain functions and terminals remain terminals. - - Arguments required: tree, branch - ''' - - if self.display == 'db': print '\n\n\033[33m *** Full Mutation: function to function *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - for n in range(len(branch)): - - # 'root' is not made available for Full mutation as this would build an entirely new Tree - - if tree[5][branch[n]] == 'func': - if self.display == 'i': print '\t\033[36m from\033[1m', tree[5][branch[n]], '\033[0;0m\033[36mto\033[1m func \033[0;0m' - - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - tree[6][branch[n]] = self.functions[rnd][0] # replace function (operator) - - elif tree[5][branch[n]] == 'term': - if self.display == 'i': print '\t\033[36m from\033[1m', tree[5][branch[n]], '\033[0;0m\033[36mto\033[1m term \033[0;0m' - - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[6][branch[n]] = self.terminals[rnd] # replace terminal (variable) - - tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data - - if self.display == 'db': print '\n\033[36m This is tourn_winner after nodes\033[1m', branch, '\033[0;0m\033[36mwere mutated and updated:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_grow_mutate(self, tree, branch): - - ''' - Mutate a branch of a Grow method Tree. - - A branch is selected within a given tree. - - If the point of mutation ('branch_top') resides at 'tree_depth_max', we do not need to grow a new tree. As the - methods for building trees always assume root (node 0) to be a function, we need only mutate this terminal node - to another terminal node, and this branch mutate method is complete. - - If the top of that branch is a terminal which does not reside at 'tree_depth_max', then it may either remain a - terminal (in which case a new value is randomly assigned) or it may mutate into a function. If it becomes a - function, a new branch (mini-tree) is generated to be appended to that nodes current location. The same is true - for function-to-function mutation. Either way, the new branch will be only as deep as allowed by the distance - from it's branch_top to the bottom of the tree. - - If however a function mutates into a terminal, the entire branch beneath the function is deleted from the array - and the entire array is updated, to fix parent/child links, associated arities, and node IDs. - - Arguments required: tree, branch - ''' - - branch_top = int(branch[0]) # replaces 2 instances, below; tested 2016 07/09 - branch_depth = self.tree_depth_max - int(tree[4][branch_top]) # 'tree_depth_max' - depth at 'branch_top' to set max potential size of new branch - 2016 07/10 - - if branch_depth < 0: # this has never occured ... yet - print '\n\t\033[31m ERROR! In fx_evolve_grow_mutate: branch_depth < 0\033[0;0m' - print '\t branch_depth =', branch_depth; self.fx_karoo_pause(0) - - elif branch_depth == 0: # the point of mutation ('branch_top') chosen resides at the maximum allowable depth, so mutate term to term - - if self.display == 'i': print '\t\033[36m max depth branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from \033[1mterm\033[0;0m \033[36mto \033[1mterm\033[0;0m\n' - if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: terminal to terminal *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[6][branch_top] = self.terminals[rnd] # replace terminal (variable) - - if self.display == 'db': print '\n\033[36m This is tourn_winner after terminal\033[1m', branch_top, '\033[0;0m\033[36mmutation, branch deletion, and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - else: # the point of mutation ('branch_top') chosen is at least one degree of depth from the maximum allowed - - # type_mod = '[func or term]' # TEST & DEBUG: force to 'func' or 'term' and comment the next 3 lines - rnd = np.random.randint(2) - if rnd == 0: type_mod = 'func' # randomly selected as Function - elif rnd == 1: type_mod = 'term' # randomly selected as Terminal - - if type_mod == 'term': # mutate 'branch_top' to a terminal and delete all nodes beneath (no subsequent nodes are added to this branch) - - if self.display == 'i': print '\t\033[36m branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from\033[1m', tree[5][branch_top], '\033[0;0m\033[36mto\033[1m term \n\033[0;0m' - if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: branch_top to terminal *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[5][branch_top] = 'term' # replace type ('func' to 'term' or 'term' to 'term') - tree[6][branch_top] = self.terminals[rnd] # replace label - - tree = np.delete(tree, branch[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') - tree = self.fx_evolve_node_arity_fix(tree) # fix all node arities - tree = self.fx_evolve_child_link_fix(tree) # fix all child links - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's - - if self.display == 'db': print '\n\033[36m This is tourn_winner after terminal\033[1m', branch_top, '\033[0;0m\033[36mmutation, branch deletion, and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - - if type_mod == 'func': # mutate 'branch_top' to a function (a new 'gp.tree' will be copied, node by node, into 'tourn_winner') - - if self.display == 'i': print '\t\033[36m branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from\033[1m', tree[5][branch_top], '\033[0;0m\033[36mto\033[1m func \n\033[0;0m' - if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: branch_top to function *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - self.fx_gen_tree_build('mutant', self.pop_tree_type, branch_depth) # build new Tree ('gp.tree') with a maximum depth which matches 'branch' - - if self.display == 'db': print '\n\033[36m This is the new Tree to be inserted at node\033[1m', branch_top, '\033[0;0m\033[36min tourn_winner:\033[0;0m\n', self.tree; self.fx_karoo_pause(0) - - # because we already know the maximum depth to which this branch can grow, there is no need to prune after insertion - tree = self.fx_evolve_branch_top_copy(tree, branch) # copy root of new 'gp.tree' to point of mutation ('branch_top') in 'tree' ('tourn_winner') - tree = self.fx_evolve_branch_body_copy(tree) # copy remaining nodes in new 'gp.tree' to 'tree' ('tourn_winner') - - tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data - - return tree - - - def fx_evolve_crossover(self, parent, branch_x, offspring, branch_y): - - ''' - Refer to the method 'fx_karoo_crossover' for a full description of the genetic operator Crossover. - - This method is called twice to produce 2 offspring per pair of parent Trees. Note that in the method - 'karoo_fx_crossover' the parent/branch relationships are swapped from the first run to the second, such that - this method receives swapped components to produce the alternative offspring. Therefore 'parent_b' is first - passed to 'offspring' which will receive 'branch_a'. With the second run, 'parent_a' is passed to 'offspring' which - will receive 'branch_b'. - - Arguments required: parent, branch_x, offspring, branch_y (parents_a / _b, branch_a / _b from 'fx_karoo_crossover') - ''' - - crossover = int(branch_x[0]) # pointer to the top of the 1st parent branch passed from 'fx_karoo_crossover' - branch_top = int(branch_y[0]) # pointer to the top of the 2nd parent branch passed from 'fx_karoo_crossover' - - if self.display == 'db': print '\n\n\033[33m *** Crossover *** \033[0;0m' - - if len(branch_x) == 1: # if the branch from the parent contains only one node (terminal) - - if self.display == 'i': print '\t\033[36m terminal crossover from \033[1mparent', parent[0][1], '\033[0;0m\033[36mto \033[1moffspring', offspring[0][1], '\033[0;0m\033[36mat node\033[1m', branch_top, '\033[0;0m' - - if self.display == 'db': - print '\n\033[36m In a copy of one parent:\033[0;0m\n', offspring - print '\n\033[36m ... we remove nodes\033[1m', branch_y, '\033[0;0m\033[36mand replace node\033[1m', branch_top, '\033[0;0m\033[36mwith a terminal from branch_x\033[0;0m'; self.fx_karoo_pause(0) - - offspring[5][branch_top] = 'term' # replace type - offspring[6][branch_top] = parent[6][crossover] # replace label with that of a particular node in 'branch_x' - offspring[8][branch_top] = 0 # set terminal arity - - offspring = np.delete(offspring, branch_y[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') - offspring = self.fx_evolve_child_link_fix(offspring) # fix all child links - offspring = self.fx_evolve_node_renum(offspring) # renumber all 'NODE_ID's - - if self.display == 'db': print '\n\033[36m This is the resulting offspring:\033[0;0m\n', offspring; self.fx_karoo_pause(0) - - - else: # we are working with a branch from 'parent' >= depth 1 (min 3 nodes) - - if self.display == 'i': print '\t\033[36m branch crossover from \033[1mparent', parent[0][1], '\033[0;0m\033[36mto \033[1moffspring', offspring[0][1], '\033[0;0m\033[36mat node\033[1m', branch_top, '\033[0;0m' - - # self.fx_gen_tree_build('test', 'f', 2) # TEST & DEBUG: disable the next 'self.tree ...' line - self.tree = self.fx_evolve_branch_copy(parent, branch_x) # generate stand-alone 'gp.tree' with properties of 'branch_x' - - if self.display == 'db': - print '\n\033[36m From one parent:\033[0;0m\n', parent - print '\n\033[36m ... we copy branch_x\033[1m', branch_x, '\033[0;0m\033[36mas a new, sub-tree:\033[0;0m\n', self.tree; self.fx_karoo_pause(0) - - if self.display == 'db': - print '\n\033[36m ... and insert it into a copy of the second parent in place of the selected branch\033[1m', branch_y,':\033[0;0m\n', offspring; self.fx_karoo_pause(0) - - offspring = self.fx_evolve_branch_top_copy(offspring, branch_y) # copy root of 'branch_y' ('gp.tree') to 'offspring' - offspring = self.fx_evolve_branch_body_copy(offspring) # copy remaining nodes in 'branch_y' ('gp.tree') to 'offspring' - offspring = self.fx_evolve_tree_prune(offspring, self.tree_depth_max) # prune to the max Tree depth + adjustment - tested 2016 07/10 - - offspring = self.fx_evolve_fitness_wipe(offspring) # wipe fitness data - - return offspring - - - def fx_evolve_branch_select(self, tree): - - ''' - Select all nodes in the 'tourn_winner' Tree at and below the randomly selected starting point. - - While Grow mutation uses this method to select a region of the 'tourn_winner' to delete, Crossover uses this - method to select a region of the 'tourn_winner' which is then converted to a stand-alone tree. As such, it is - imperative that the nodes be in the correct order, else all kinds of bad things happen. - - Arguments required: tree - ''' - - branch = np.array([]) # the array is necessary in order to len(branch) when 'branch' has only one element - branch_top = np.random.randint(2, len(tree[3])) # randomly select a non-root node - branch_eval = self.fx_eval_id(tree, branch_top) # generate tuple of 'branch_top' and subseqent nodes - branch_symp = sympify(branch_eval) # convert string into something useful - branch = np.append(branch, branch_symp) # append list to array - - branch = np.sort(branch) # sort nodes in branch for Crossover. - - if self.display == 'i': print '\t \033[36mwith nodes\033[1m', branch, '\033[0;0m\033[36mchosen for mutation\033[0;0m' - - return branch - - - def fx_evolve_branch_top_copy(self, tree, branch): - - ''' - Copy the point of mutation ('branch_top') from 'gp.tree' to 'tree'. - - This method works with 3 inputs: local 'tree' is being modified; local 'branch' is a section of 'tree' which - will be removed; and global 'gp.tree' (recycling from initial population generation) is the new Tree to be - copied into 'tree', replacing 'branch'. - - This method is used in both Grow Mutation and Crossover. - - Arguments required: tree, branch - ''' - - branch_top = int(branch[0]) - - tree[5][branch_top] = 'func' # update type ('func' to 'term' or 'term' to 'term'); this modifies gp.tree[5[1] from 'root' to 'func' - tree[6][branch_top] = self.tree[6][1] # copy node_label from new tree - tree[8][branch_top] = self.tree[8][1] # copy node_arity from new tree - - tree = np.delete(tree, branch[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') - - c_buffer = self.fx_evolve_c_buffer(tree, branch_top) # generate c_buffer for point of mutation ('branch_top') - tree = self.fx_evolve_child_insert(tree, branch_top, c_buffer) # insert new nodes - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's - - if self.display == 'db': - print '\n\t ... inserted node 1 of', len(self.tree[3])-1 - print '\n\033[36m This is the Tree after a new node is inserted:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_branch_body_copy(self, tree): - - ''' - Copy the body of 'gp.tree' to 'tree', one node at a time. - - This method works with 3 inputs: local 'tree' is being modified; local 'branch' is a section of 'tree' which - will be removed; and global 'gp.tree' (recycling from initial population generation) is the new Tree to be - copied into 'tree', replacing 'branch'. - - This method is used in both Grow Mutation and Crossover. - - Arguments required: tree - ''' - - node_count = 2 # set node count for 'gp.tree' to 2 as the new root has already replaced 'branch_top' in 'fx_evolve_branch_top_copy' - - while node_count < len(self.tree[3]): # increment through all nodes in the new Tree ('gp.tree'), starting with node 2 - - for j in range(1, len(tree[3])): # increment through all nodes in tourn_winner ('tree') - - if self.display == 'db': print '\tScanning tourn_winner node_id:', j - - if tree[5][j] == '': - tree[5][j] = self.tree[5][node_count] # copy 'node_type' from branch to tree - tree[6][j] = self.tree[6][node_count] # copy 'node_label' from branch to tree - tree[8][j] = self.tree[8][node_count] # copy 'node_arity' from branch to tree - - if tree[5][j] == 'term': - tree = self.fx_evolve_child_link_fix(tree) # fix all child links - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's - - if tree[5][j] == 'func': - c_buffer = self.fx_evolve_c_buffer(tree, j) # generate 'c_buffer' for point of mutation ('branch_top') - tree = self.fx_evolve_child_insert(tree, j, c_buffer) # insert new nodes - tree = self.fx_evolve_child_link_fix(tree) # fix all child links - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's - - if self.display == 'db': - print '\n\t ... inserted node', node_count, 'of', len(self.tree[3])-1 - print '\n\033[36m This is the Tree after a new node is inserted:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - node_count = node_count + 1 # exit loop when 'node_count' reaches the number of columns in the array 'gp.tree' - - return tree - - - def fx_evolve_branch_copy(self, tree, branch): - - ''' - This method prepares a stand-alone Tree as a copy of the given branch. - - This method is used with Crossover. - - Arguments required: tree, branch - ''' - - new_tree = np.array([ ['TREE_ID'],['tree_type'],['tree_depth_base'],['NODE_ID'],['node_depth'],['node_type'],['node_label'],['node_parent'],['node_arity'],['node_c1'],['node_c2'],['node_c3'],['fitness'] ]) - - # tested 2015 06/08 - for n in range(len(branch)): - - node = branch[n] - branch_top = int(branch[0]) - - TREE_ID = 'copy' - tree_type = tree[1][1] - tree_depth_base = int(tree[4][branch[-1]]) - int(tree[4][branch_top]) # subtract depth of 'branch_top' from the last in 'branch' - NODE_ID = tree[3][node] - node_depth = int(tree[4][node]) - int(tree[4][branch_top]) # subtract the depth of 'branch_top' from the current node depth - node_type = tree[5][node] - node_label = tree[6][node] - node_parent = '' # updated by 'fx_evolve_parent_link_fix', below - node_arity = tree[8][node] - node_c1 = '' # updated by 'fx_evolve_child_link_fix', below - node_c2 = '' - node_c3 = '' - fitness = '' - - new_tree = np.append(new_tree, [ [TREE_ID],[tree_type],[tree_depth_base],[NODE_ID],[node_depth],[node_type],[node_label],[node_parent],[node_arity],[node_c1],[node_c2],[node_c3],[fitness] ], 1) - - new_tree = self.fx_evolve_node_renum(new_tree) - new_tree = self.fx_evolve_child_link_fix(new_tree) - new_tree = self.fx_evolve_parent_link_fix(new_tree) - new_tree = self.fx_archive_tree_clean(new_tree) - - return new_tree - - - def fx_evolve_c_buffer(self, tree, node): - - ''' - This method serves the very important function of determining the links from parent to child for any given - node. The single, simple formula [parent_arity_sum + prior_sibling_arity - prior_siblings] perfectly determines - the correct position of the child node, already in place or to be inserted, no matter the depth nor complexity - of the tree. - - This method is currently called from the evolution methods, but will soon (I hope) be called from the first - generation Tree generation methods (above) such that the same method may be used repeatedly. - - Arguments required: tree, node - ''' - - parent_arity_sum = 0 - prior_sibling_arity = 0 - prior_siblings = 0 - - for n in range(1, len(tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if int(tree[4][n]) == int(tree[4][node])-1: # find parent nodes at the prior depth - if tree[8][n] != '': parent_arity_sum = parent_arity_sum + int(tree[8][n]) # sum arities of all parent nodes at the prior depth - - if int(tree[4][n]) == int(tree[4][node]) and int(tree[3][n]) < int(tree[3][node]): # find prior siblings at the current depth - if tree[8][n] != '': prior_sibling_arity = prior_sibling_arity + int(tree[8][n]) # sum prior sibling arity - prior_siblings = prior_siblings + 1 # sum quantity of prior siblings - - c_buffer = node + (parent_arity_sum + prior_sibling_arity - prior_siblings) # One algo to rule the world! - - return c_buffer - - - def fx_evolve_child_link(self, tree, node, c_buffer): - - ''' - Link each parent node to its children. - - Arguments required: tree, node, c_buffer - ''' - - if int(tree[3][node]) == 1: c_buffer = c_buffer + 1 # if root (node 1) is passed through this method - - if tree[8][node] != '': - - if int(tree[8][node]) == 0: # if arity = 0 - tree[9][node] = '' - tree[10][node] = '' - tree[11][node] = '' - - elif int(tree[8][node]) == 1: # if arity = 1 - tree[9][node] = c_buffer - tree[10][node] = '' - tree[11][node] = '' - - elif int(tree[8][node]) == 2: # if arity = 2 - tree[9][node] = c_buffer - tree[10][node] = c_buffer + 1 - tree[11][node] = '' - - elif int(tree[8][node]) == 3: # if arity = 3 - tree[9][node] = c_buffer - tree[10][node] = c_buffer + 1 - tree[11][node] = c_buffer + 2 - - else: print '\n\t\033[31m ERROR! In fx_evolve_child_link: node', node, 'has arity', tree[8][node]; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_child_link_fix(self, tree): - - ''' - In a given Tree, fix 'node_c1', 'node_c2', 'node_c3' for all nodes. - - This is required anytime the size of the array 'gp.tree' has been modified, as with both Grow and Full mutation. - - Arguments required: tree - ''' - - # tested 2015 06/04 - for node in range(1, len(tree[3])): - - c_buffer = self.fx_evolve_c_buffer(tree, node) # generate c_buffer for each node - tree = self.fx_evolve_child_link(tree, node, c_buffer) # update child links for each node - - return tree - - - def fx_evolve_child_insert(self, tree, node, c_buffer): - - ''' - Insert child nodes. - - Arguments required: tree, node, c_buffer - ''' - - if int(tree[8][node]) == 0: # if arity = 0 - print '\n\t\033[31m ERROR! In fx_evolve_child_insert: node', node, 'has arity 0\033[0;0m'; self.fx_karoo_pause(0) - - elif int(tree[8][node]) == 1: # if arity = 1 - tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' - tree[3][c_buffer] = c_buffer # node ID - tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer] = int(tree[3][node]) # parent ID - - elif int(tree[8][node]) == 2: # if arity = 2 - tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' - tree[3][c_buffer] = c_buffer # node ID - tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer] = int(tree[3][node]) # parent ID - - tree = np.insert(tree, c_buffer + 1, '', axis=1) # insert node for 'node_c2' - tree[3][c_buffer + 1] = c_buffer + 1 # node ID - tree[4][c_buffer + 1] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer + 1] = int(tree[3][node]) # parent ID - - elif int(tree[8][node]) == 3: # if arity = 3 - tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' - tree[3][c_buffer] = c_buffer # node ID - tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer] = int(tree[3][node]) # parent ID - - tree = np.insert(tree, c_buffer + 1, '', axis=1) # insert node for 'node_c2' - tree[3][c_buffer + 1] = c_buffer + 1 # node ID - tree[4][c_buffer + 1] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer + 1] = int(tree[3][node]) # parent ID - - tree = np.insert(tree, c_buffer + 2, '', axis=1) # insert node for 'node_c3' - tree[3][c_buffer + 2] = c_buffer + 2 # node ID - tree[4][c_buffer + 2] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer + 2] = int(tree[3][node]) # parent ID - - else: print '\n\t\033[31m ERROR! In fx_evolve_child_insert: node', node, 'arity > 3\033[0;0m'; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_parent_link_fix(self, tree): - - ''' - In a given Tree, fix 'parent_id' for all nodes. - - This is automatically handled in all mutations except with Crossover due to the need to copy branches 'a' and - 'b' to their own trees before inserting them into copies of the parents. - - Technically speaking, the 'node_parent' value is not used by any methods. The parent ID can be completely out - of whack and the expression will work perfectly. This is maintained for the sole purpose of granting the user - a friendly, makes-sense interface which can be read in both directions. - - Arguments required: tree - ''' - - ### THIS METHOD MAY NOT BE REQUIRED AS SORTING 'branch' SEEMS TO HAVE FIXED 'parent_id' ### - - # tested 2015 06/05 - for node in range(1, len(tree[3])): - - if tree[9][node] != '': - child = int(tree[9][node]) - tree[7][child] = node - - if tree[10][node] != '': - child = int(tree[10][node]) - tree[7][child] = node - - if tree[11][node] != '': - child = int(tree[11][node]) - tree[7][child] = node - - return tree - - - def fx_evolve_node_arity_fix(self, tree): - - ''' - In a given Tree, fix 'node_arity' for all nodes labeled 'term' but with arity 2. - - This is required after a function has been replaced by a terminal, as may occur with both Grow mutation and - Crossover. - - Arguments required: tree - ''' - - # tested 2015 05/31 - for n in range(1, len(tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if tree[5][n] == 'term': # check for discrepency - tree[8][n] = '0' # set arity to 0 - tree[9][n] = '' # wipe 'node_c1' - tree[10][n] = '' # wipe 'node_c2' - tree[11][n] = '' # wipe 'node_c3' - - return tree - - - def fx_evolve_node_renum(self, tree): - - ''' - Renumber all 'NODE_ID' in a given tree. - - This is required after a new generation is evolved as the NODE_ID numbers are carried forward from the previous - generation but are no longer in order. - - Arguments required: tree - ''' - - for n in range(1, len(tree[3])): - - tree[3][n] = n # renumber all Trees in given population - - return tree - - - def fx_evolve_fitness_wipe(self, tree): - - ''' - Remove all fitness data from a given tree. - - This is required after a new generation is evolved as the fitness of the same Tree prior to its mutation will - no longer apply. - - Arguments required: tree - ''' - - tree[12][1:] = '' # wipe fitness data - - return tree - - - def fx_evolve_tree_prune(self, tree, depth): - - ''' - This method reduces the depth of a Tree. Used with Crossover, the input value 'branch' can be a partial Tree - (branch) or a full tree, and it will operate correctly. The input value 'depth' becomes the new maximum depth, - where depth is defined as the local maximum + the user defined adjustment. - - Arguments required: tree, depth - ''' - - nodes = [] - - # tested 2015 06/08 - for n in range(1, len(tree[3])): - - if int(tree[4][n]) == depth and tree[5][n] == 'func': - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[5][n] = 'term' # mutate type 'func' to 'term' - tree[6][n] = self.terminals[rnd] # replace label - - elif int(tree[4][n]) > depth: # record nodes deeper than the maximum allowed Tree depth - nodes.append(n) - - else: pass # as int(tree[4][n]) < depth and will remain untouched - - tree = np.delete(tree, nodes, axis = 1) # delete nodes deeper than the maximum allowed Tree depth - tree = self.fx_evolve_node_arity_fix(tree) # fix all node arities - - return tree - - - def fx_evolve_tree_renum(self, population): - - ''' - Renumber all 'TREE_ID' in a given population. - - This is required after a new generation is evolved as the TREE_ID numbers are carried forward from the previous - generation but are no longer in order. - - Arguments required: population - ''' - - for tree_id in range(1, len(population)): - - population[tree_id][0][1] = tree_id # renumber all Trees in given population - - return population - - - def fx_evolve_pop_copy(self, pop_a, title): - - ''' - Copy one population to another. - - Simply copying a list of arrays generates a pointer to the original list. Therefore we must append each array - to a new, empty array and then build a list of those new arrays. - - Arguments required: pop_a, title - ''' - - pop_b = [title] # an empty list stores a copy of the prior generation - - for tree in range(1, len(pop_a)): # increment through each Tree in the current population - - tree_copy = np.copy(pop_a[tree]) # copy each array in the current population - pop_b.append(tree_copy) # add each copied Tree to the new population list - - return pop_b - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Display a Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_display_tree(self, tree): - - ''' - Display all or part of a Tree on-screen. - - This method displays all sequential node_ids from 'start' node through bottom, within the given tree. - - Arguments required: tree - ''' - - ind = '' - print '\n\033[1m\033[36m Tree ID', int(tree[0][1]), '\033[0;0m' - - for depth in range(0, self.tree_depth_max + 1): # increment through all possible Tree depths - tested 2016 07/09 - print '\n', ind,'\033[36m Tree Depth:', depth, 'of', tree[2][1], '\033[0;0m' - - for node in range(1, len(tree[3])): # increment through all nodes (redundant, I know) - if int(tree[4][node]) == depth: - print '' - print ind,'\033[1m\033[36m NODE:', tree[3][node], '\033[0;0m' - print ind,' type:', tree[5][node] - print ind,' label:', tree[6][node], '\tparent node:', tree[7][node] - print ind,' arity:', tree[8][node], '\tchild node(s):', tree[9][node], tree[10][node], tree[11][node] - - ind = ind + '\t' - - print '' - self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree - print '\t\033[36mTree', tree[0][1], 'yields (raw):', self.algo_raw, '\033[0;0m' - print '\t\033[36mTree', tree[0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' - - return - - - def fx_display_branch(self, tree, start): - - ''' - Display a Tree branch on-screen. - - This method displays all sequential node_ids from 'start' node through bottom, within the given branch. - - This method is not used by Karoo GP at this time. - - Arguments required: tree, start - ''' - - branch = np.array([]) # the array is necessary in order to len(branch) when 'branch' has only one element - branch_eval = self.fx_eval_id(tree, start) # generate tuple of given 'branch' - branch_symp = sympify(branch_eval) # convert string from tuple to list - branch = np.append(branch, branch_symp) # append list to array - ind = '' - - # for depth in range(int(tree[4][start]), int(tree[2][1]) + self.tree_depth_max + 1): # increment through all Tree depths - tested 2016 07/09 - for depth in range(int(tree[4][start]), self.tree_depth_max + 1): # increment through all Tree depths - tested 2016 07/09 - print '\n', ind,'\033[36m Tree Depth:', depth, 'of', tree[2][1], '\033[0;0m' - - for n in range(0, len(branch)): # increment through all nodes listed in the branch - node = branch[n] - - if int(tree[4][node]) == depth: - print '' - print ind,'\033[1m\033[36m NODE:', node, '\033[0;0m' - print ind,' type:', tree[5][node] - print ind,' label:', tree[6][node], '\tparent node:', tree[7][node] - print ind,' arity:', tree[8][node], '\tchild node(s):', tree[9][node], tree[10][node], tree[11][node] - - ind = ind + '\t' - - print '' - self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree - print '\t\033[36mTree', tree[0][1], 'yields (raw):', self.algo_raw, '\033[0;0m' - print '\t\033[36mTree', tree[0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' - - return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Archive | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_archive_tree_clean(self, tree): - - ''' - This method aesthetically cleans the Tree array, removing redundant data. - - Arguments required: tree - ''' - - tree[0][2:] = '' # A little clean-up to make things look pretty :) - tree[1][2:] = '' # Ignore the man behind the curtain! - tree[2][2:] = '' # Yes, I am a bit OCD ... but you *know* you appreciate clean arrays. - - return tree - - - def fx_archive_tree_append(self, tree): - - ''' - Append Tree array to the foundation Population. - - Arguments required: tree - ''' - - self.fx_archive_tree_clean(tree) # clean 'tree' prior to storing - self.population_a.append(tree) # append 'tree' to population list - - return - - - def fx_archive_tree_write(self, population, key): - - ''' - Save population_* to disk. - - Arguments required: population, key - ''' - - with open(self.filename[key], 'a') as csv_file: - target = csv.writer(csv_file, delimiter=',') - if self.generation_id != 1: target.writerows(['']) # empty row before each generation - target.writerows([['Karoo GP by Kai Staats', 'Generation:', str(self.generation_id)]]) - - for tree in range(1, len(population)): - target.writerows(['']) # empty row before each Tree - for row in range(0, 13): # increment through each row in the array Tree - target.writerows([population[tree][row]]) - - - def fx_archive_params_write(self, app): # tested 2017 02/13 - - ''' - Save run-time configuration parameters to disk. - - Arguments required: none - ''' - - file = open(self.path + '/log_config.txt', 'w') - file.write('Karoo GP ' + app) - file.write('\n launched: ' + str(self.datetime)) - file.write('\n dataset: ' + str(self.dataset)) - file.write('\n') - file.write('\n kernel: ' + str(self.kernel)) - file.write('\n precision: ' + str(self.precision)) - file.write('\n') - # file.write('tree type: ' + tree_type) - # file.write('tree depth base: ' + str(tree_depth_base)) - file.write('\n tree depth max: ' + str(self.tree_depth_max)) - file.write('\n min node count: ' + str(self.tree_depth_min)) - file.write('\n') - file.write('\n genetic operator Reproduction: ' + str(self.evolve_repro)) - file.write('\n genetic operator Point Mutation: ' + str(self.evolve_point)) - file.write('\n genetic operator Branch Mutation: ' + str(self.evolve_branch)) - file.write('\n genetic operator Crossover: ' + str(self.evolve_cross)) - file.write('\n') - file.write('\n tournament size: ' + str(self.tourn_size)) - file.write('\n population: ' + str(self.tree_pop_max)) - file.write('\n number of generations: ' + str(self.generation_id)) - file.write('\n\n') - file.close() - - - file = open(self.path + '/log_test.txt', 'w') - file.write('Karoo GP ' + app) - file.write('\n launched: ' + str(self.datetime)) - file.write('\n dataset: ' + str(self.dataset)) - file.write('\n') - - if len(self.fittest_dict) > 0: - - fitness_best = 0 - fittest_tree = 0 - - # original method, using pre-built fittest_dict - # file.write('\n The leading Trees and their associated expressions are:') - # for n in sorted(self.fittest_dict): - # file.write('\n\t ' + str(n) + ' : ' + str(self.fittest_dict[n])) - - # revised method, re-evaluating all Trees from stored fitness score - for tree_id in range(1, len(self.population_b)): - - fitness = float(self.population_b[tree_id][12][1]) - - if self.kernel == 'c': # display best fit Trees for the CLASSIFY kernel - if fitness >= fitness_best: # find the Tree with Maximum fitness score - fitness_best = fitness; fittest_tree = tree_id # set best fitness Tree - - elif self.kernel == 'r': # display best fit Trees for the REGRESSION kernel - if fitness_best == 0: fitness_best = fitness # set the baseline first time through - if fitness <= fitness_best: # find the Tree with Minimum fitness score - fitness_best = fitness; fittest_tree = tree_id # set best fitness Tree - - elif self.kernel == 'm': # display best fit Trees for the MATCH kernel - if fitness == self.data_train_rows: # find the Tree with a perfect match for all data rows - fitness_best = fitness; fittest_tree = tree_id # set best fitness Tree - - # elif self.kernel == '[other]': # display best fit Trees for the [other] kernel - # if fitness [>=, <=] fitness_best: # find the Tree with [Maximum or Minimum] fitness score - # fitness_best = fitness; fittest_tree = tree_id # set best fitness Tree - - # print 'fitness_best:', fitness_best, 'fittest_tree:', fittest_tree - - - # test the most fit Tree and write to the .txt log - self.fx_eval_poly(self.population_b[int(fittest_tree)]) # generate the raw and sympified equation for the given Tree using SymPy - expr = str(self.algo_sym) # get simplified expression and process it by TF - tested 2017 02/02 - result = self.fx_fitness_eval(expr, self.data_test, get_labels=True) - - file.write('\n\n Tree ' + str(fittest_tree) + ' is the most fit, with expression:') - file.write('\n\n ' + str(self.algo_sym)) - - if self.kernel == 'c': - file.write('\n\n Classification fitness score: {}'.format(result['fitness'])) - file.write('\n\n Precision-Recall report:\n {}'.format(skm.classification_report(result['solution'], result['labels'][0]))) - file.write('\n Confusion matrix:\n {}'.format(skm.confusion_matrix(result['solution'], result['labels'][0]))) - - elif self.kernel == 'r': - MSE, fitness = skm.mean_squared_error(result['result'], result['solution']), result['fitness'] - file.write('\n\n Regression fitness score: {}'.format(fitness)) - file.write('\n Mean Squared Error: {}'.format(MSE)) - - elif self.kernel == 'm': - file.write('\n\n Matching fitness score: {}'.format(result['fitness'])) - - # elif self.kernel == '[other]': - # file.write( ... ) - - else: file.write('\n\n There were no evolved solutions generated in this run... your species has gone extinct!') - - file.write('\n\n') - file.close() - - return - - diff --git a/karoo_gp/karoo_gp_main.py b/karoo_gp/karoo_gp_main.py deleted file mode 100644 index bd176a4..0000000 --- a/karoo_gp/karoo_gp_main.py +++ /dev/null @@ -1,257 +0,0 @@ -# Karoo GP Main (desktop) -# Use Genetic Programming for Classification and Symbolic Regression -# by Kai Staats, MSc; see LICENSE.md -# Thanks to Emmanuel Dufourq and Arun Kumar for support during 2014-15 devel; TensorFlow support provided by Iurii Milovanov -# version 1.0.3 - -''' -A word to the newbie, expert, and brave-- -Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo User Guide' -before running this application. While your computer will not burst into flames nor will the sun collapse into a black -hole if you do not, you will likely find more enjoyment of this particular flavour of GP with a little understanding -of its intent and design. - -KAROO GP DESKTOP -This is the Karoo GP desktop application. It presents a simple yet functional user interface for configuring each -Karoo GP run. While this can be launched on a remote server, you may find that once you get the hang of using Karoo, -and are in more of a production mode than one of experimentation, using karoo_gp_server.py is more to your liking as -it provides both a scripted and/or command-line launch vehicle. - -To launch Karoo GP desktop: - - $ python karoo_gp_main.py - - (or from iPython) - - $ run karoo_gp_main.py - - -If you include the path to an external dataset, it will auto-load at launch: - - $ python karoo_gp_main.py /[path]/[to_your]/[filename].csv -''' - -import sys # sys.path.append('modules/') to add the directory 'modules' to the current path -import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP() -import time - -#++++++++++++++++++++++++++++++++++++++++++ -# User Defined Configuration | -#++++++++++++++++++++++++++++++++++++++++++ - -''' -Karoo GP queries the user for key parameters, some of which may be adjusted during run-time -at user invoked pauses. See the User Guide for meaning and value of each of the following parameters. - -Future versions will enable all of these parameters to be configured via an external configuration file and/or -command-line arguments passed at launch. -''' - -gp.karoo_banner() - -print '' - -menu = ['c','r','m','p',''] -while True: - try: - gp.kernel = raw_input('\t Select (c)lassification, (r)egression, (m)atching, or (p)lay (default m): ') - if gp.kernel not in menu: raise ValueError() - gp.kernel = gp.kernel or 'm'; break - except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - -if gp.kernel == 'p': - - menu = ['f','g',''] - while True: - try: - tree_type = raw_input('\t Select (f)ull or (g)row method (default f): ') - if tree_type not in menu: raise ValueError() - tree_type = tree_type or 'f'; break - except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - -else: - - menu = ['f','g','r',''] - while True: - try: - tree_type = raw_input('\t Select (f)ull, (g)row, or (r)amped 50/50 method (default r): ') - if tree_type not in menu: raise ValueError() - tree_type = tree_type or 'r'; break - except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - -menu = range(1,11) -while True: - try: - tree_depth_base = raw_input('\t Enter depth of the \033[3minitial\033[0;0m population of Trees (default 3): ') - if tree_depth_base not in str(menu) or tree_depth_base == '0': raise ValueError() - tree_depth_base = tree_depth_base or 3; tree_depth_base = int(tree_depth_base); break - except ValueError: print '\t\033[32m Enter a number from 1 including 10. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - - - -if gp.kernel == 'p': # if the Play kernel is selected - gp.tree_depth_max = tree_depth_base - gp.tree_pop_max = 1 - gp.display = 'm' - -else: # if any other kernel is selected - - if tree_type == 'f': gp.tree_depth_max = tree_depth_base - else: # if type is Full, the maximum Tree depth for the full run is equal to the initial population - - menu = range(tree_depth_base,11) - while True: - try: - gp.tree_depth_max = raw_input('\t Enter maximum Tree depth (default matches \033[3minitial\033[0;0m): ') - if gp.tree_depth_max not in str(menu) or gp.tree_depth_max == '0': raise ValueError() - gp.tree_depth_max = gp.tree_depth_max or tree_depth_base; gp.tree_depth_max = int(gp.tree_depth_max); break - # gp.tree_depth_max = int(gp.tree_depth_max) - tree_depth_base; break - except ValueError: print '\t\033[32m Enter a number >= the maximum Tree depth. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - - menu = range(3,101) - while True: - try: - gp.tree_depth_min = raw_input('\t Enter minimum number of nodes for any given Tree (default 3): ') - if gp.tree_depth_min not in str(menu) or gp.tree_depth_min == '0': raise ValueError() - gp.tree_depth_min = gp.tree_depth_min or 3; gp.tree_depth_min = int(gp.tree_depth_min); break - except ValueError: print '\t\033[32m Enter a number from 3 to 2^(depth + 1) - 1 including 100. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - - menu = range(10,1001) - while True: - try: - gp.tree_pop_max = raw_input('\t Enter number of Trees in each population (default 100): ') - if gp.tree_pop_max not in str(menu) or gp.tree_pop_max == '0': raise ValueError() - gp.tree_pop_max = gp.tree_pop_max or 100; gp.tree_pop_max = int(gp.tree_pop_max); break - except ValueError: print '\t\033[32m Enter a number from 10 including 1000. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - - menu = range(1,101) - while True: - try: - gp.generation_max = raw_input('\t Enter max number of generations (default 10): ') - if gp.generation_max not in str(menu) or gp.generation_max == '0': raise ValueError() - gp.generation_max = gp.generation_max or 10; gp.generation_max = int(gp.generation_max); break - except ValueError: print '\t\033[32m Enter a number from 1 including 100. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - - menu = ['i','g','m','s','db',''] - while True: - try: - gp.display = raw_input('\t Display (i)nteractive, (g)eneration, (m)iminal, (s)ilent, or (d)e(b)ug (default m): ') - if gp.display not in menu: raise ValueError() - gp.display = gp.display or 'm'; break - except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m' - except KeyboardInterrupt: sys.exit() - - -# define the ratio between types of mutation, where all sum to 1.0; can be adjusted in 'i'nteractive mode -gp.evolve_repro = int(0.1 * gp.tree_pop_max) # quantity of a population generated through Reproduction -gp.evolve_point = int(0.0 * gp.tree_pop_max) # quantity of a population generated through Point Mutation -gp.evolve_branch = int(0.2 * gp.tree_pop_max) # quantity of a population generated through Branch Mutation -gp.evolve_cross = int(0.7 * gp.tree_pop_max) # quantity of a population generated through Crossover - -gp.tourn_size = 10 # qty of individuals entered into each tournament (standard 10); can be adjusted in 'i'nteractive mode -gp.precision = 10 # the number of floating points for the round function in 'fx_fitness_eval'; hard coded - - -#++++++++++++++++++++++++++++++++++++++++++ -# Construct First Generation of Trees | -#++++++++++++++++++++++++++++++++++++++++++ - -''' -Karoo GP constructs the first generation of Trees. All subsequent generations evolve from priors, with no new Trees -constructed from scratch. All parameters which define the Trees were set by the user in the previous section. - -If the user has selected 'Play' mode, this is the only generation to be constructed, and then GP Karoo terminates. -''' - -start = time.time() # start the clock for the timer - -filename = '' # temp place holder -gp.fx_karoo_data_load(tree_type, tree_depth_base, filename) -gp.generation_id = 1 # set initial generation ID - -gp.population_a = ['Karoo GP by Kai Staats, Generation ' + str(gp.generation_id)] # an empty list which will store all Tree arrays, one generation at a time - -gp.fx_karoo_construct(tree_type, tree_depth_base) # construct the first population of Trees - -if gp.kernel != 'p': print '\n We have constructed a population of', gp.tree_pop_max,'Trees for Generation 1\n' - -else: # EOL for Play mode - gp.fx_display_tree(gp.tree) # print the current Tree - gp.fx_archive_tree_write(gp.population_a, 'a') # save this one Tree to disk - sys.exit() - - -#++++++++++++++++++++++++++++++++++++++++++ -# Evaluate First Generation of Trees | -#++++++++++++++++++++++++++++++++++++++++++ - -''' -Karoo GP evaluates the first generation of Trees. This process flattens each GP Tree into a standard -equation by means of a recursive algorithm and subsequent processing by the SymPy library which -simultaneously evaluates the Tree for its results, returns null for divide by zero, reorganises -and then rewrites the expression in its simplest form. - -If the user has defined only 1 generation, then this is the end of the run. Else, Karoo GP -continues into multi-generational evolution. -''' - -if gp.display != 's': - print ' Evaluate the first generation of Trees ...' - if gp.display == 'i': gp.fx_karoo_pause(0) - -gp.fx_fitness_gym(gp.population_a) # generate expression, evaluate fitness, compare fitness -gp.fx_archive_tree_write(gp.population_a, 'a') # save the first generation of Trees to disk - -# no need to continue if only 1 generation or fewer than 10 Trees were designated by the user -if gp.tree_pop_max < 10 or gp.generation_max == 1: - gp.fx_archive_params_write('Desktop') # save run-time parameters to disk - gp.fx_karoo_eol() - sys.exit() - - -#++++++++++++++++++++++++++++++++++++++++++ -# Evolve Multiple Generations | -#++++++++++++++++++++++++++++++++++++++++++ - -''' -Karoo GP moves into multi-generational evolution. - -In the following four evolutionary methods, the global list of arrays 'gp.population_a' is repeatedly recycled as -the prior generation from which the local list of arrays 'gp.population_b' is created, one array at a time. The ratio of -invocation of the four evolutionary processes for each generation is set by the parameters in the 'User Defined -Configuration' (top). -''' - -for gp.generation_id in range(2, gp.generation_max + 1): # loop through 'generation_max' - - print '\n Evolve a population of Trees for Generation', gp.generation_id, '...' - gp.population_b = ['GP Tree by Kai Staats, Evolving Generation'] # initialise population_b to host the next generation - - gp.fx_fitness_gene_pool() # generate the viable gene pool (compares against gp.tree_depth_min) - gp.fx_karoo_reproduce() # method 1 - Reproduction - gp.fx_karoo_point_mutate() # method 2 - Point Mutation - gp.fx_karoo_branch_mutate() # method 3 - Branch Mutation - gp.fx_karoo_crossover() # method 4 - Crossover Reproduction - gp.fx_eval_generation() # evaluate all Trees in a single generation - - gp.population_a = gp.fx_evolve_pop_copy(gp.population_b, ['GP Tree by Kai Staats, Generation ' + str(gp.generation_id)]) - - -#++++++++++++++++++++++++++++++++++++++++++ -# "End of line, man!" --CLU | -#++++++++++++++++++++++++++++++++++++++++++ - -print '\n \033[36m Karoo GP has an ellapsed time of \033[0;0m\033[31m%f\033[0;0m' % (time.time() - start), '\033[0;0m' - -gp.fx_archive_tree_write(gp.population_b, 'f') # save the final generation of Trees to disk -gp.fx_karoo_eol() - - diff --git a/karoo_gp/karoo_gp_server.py b/karoo_gp/karoo_gp_server.py deleted file mode 100644 index 671b8ce..0000000 --- a/karoo_gp/karoo_gp_server.py +++ /dev/null @@ -1,89 +0,0 @@ -# Karoo GP Server -# Use Genetic Programming for Classification and Symbolic Regression -# by Kai Staats, MSc; see LICENSE.md -# Thanks to Emmanuel Dufourq and Arun Kumar for support during 2014-15 devel; TensorFlow support provided by Iurii Milovanov -# version 1.0.3 - -''' -A word to the newbie, expert, and brave-- -Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo User Guide' -before running this application. While your computer will not burst into flames nor will the sun collapse into a black -hole if you do not, you will likely find more enjoyment of this particular flavour of GP with a little understanding -of its intent and design. - -KAROO GP SERVER -This is the Karoo GP server application. It can be internally scripted, fully command-line configured, or a combination -of both. If this is your first time using Karoo GP, please run the desktop application karoo_gp_main.py first in order -that you come to understand the full functionality of this particular Genetic Programming platform. - -To launch Karoo GP server: - - $ python karoo_gp_server.py - - (or from iPython) - - $ run karoo_gp_server.py - - -Without any arguments, Karoo GP relies entirely upon the scripted settings and the datasets located in karoo_gp/files/. - -If you include the path to an external dataset, it will auto-load at launch: - - $ python karoo_gp_server.py /[path]/[to_your]/[filename].csv - - -You can include a number of additional arguments which override the default values, as follows: - - -ker [r,c,m] fitness function: (r)egression, (c)lassification, or (m)atching - -typ [f,g,r] Tree type: (f)ull, (g)row, or (r)amped half/half - -bas [3...10] maximum Tree depth for the initial population - -max [3...10] maximum Tree depth for the entire run - -min [3...100] minimum number of nodes - -pop [10...1000] maximum population - -gen [1...100] number of generations - -Note that if you include any of the above flags, then you must also include a flag to load an external dataset: - - $ python karoo_gp_server.py -ker c -typ r -bas 4 -fil /[path]/[to_your]/[filename].csv -''' - -import sys # sys.path.append('modules/') to add the directory 'modules' to the current path -import argparse -import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP() - -ap = argparse.ArgumentParser(description = 'Karoo GP Server') -ap.add_argument('-ker', action = 'store', dest = 'kernel', default = 'm', help = '[c,r,m] fitness function: (r)egression, (c)lassification, or (m)atching') -ap.add_argument('-typ', action = 'store', dest = 'type', default = 'r', help = '[f,g,r] Tree type: (f)ull, (g)row, or (r)amped half/half') -ap.add_argument('-bas', action = 'store', dest = 'depth_base', default = 5, help = '[3...10] maximum Tree depth for the initial population') -ap.add_argument('-max', action = 'store', dest = 'depth_max', default = 5, help = '[3...10] maximum Tree depth for the entire run') -ap.add_argument('-min', action = 'store', dest = 'depth_min', default = 3, help = '[3...100] minimum number of nodes') -ap.add_argument('-pop', action = 'store', dest = 'pop_max', default = 100, help = '[10...1000] maximum population') -ap.add_argument('-gen', action = 'store', dest = 'gen_max', default = 30, help = '[1...100] number of generations') -ap.add_argument('-tor', action = 'store', dest = 'tor_size', default = 10, help = '[1...max pop] tournament size') -ap.add_argument('-fil', action = 'store', dest = 'filename', default = 'files/data_MATCH.csv', help = '/path/to_your/[data].csv') - -args = ap.parse_args() - -# pass the argparse defaults and/or user inputs to the required variables -gp.kernel = str(args.kernel) -tree_type = str(args.type) -tree_depth_base = int(args.depth_base) -gp.tree_depth_max = int(args.depth_max) -gp.tree_depth_min = int(args.depth_min) -gp.tree_pop_max = int(args.pop_max) -gp.generation_max = int(args.gen_max) -filename = str(args.filename) - -gp.display = 's' # display mode is set to (s)ilent -gp.evolve_repro = int(0.1 * gp.tree_pop_max) # quantity of a population generated through Reproduction -gp.evolve_point = int(0.0 * gp.tree_pop_max) # quantity of a population generated through Point Mutation -gp.evolve_branch = int(0.2 * gp.tree_pop_max) # quantity of a population generated through Branch Mutation -gp.evolve_cross = int(0.7 * gp.tree_pop_max) # quantity of a population generated through Crossover - -gp.tourn_size = int(args.tor_size) # qty of individuals entered into each tournament; can be adjusted in 'i'nteractive mode -gp.precision = 4 # the number of floating points for the round function in 'fx_fitness_eval' - -# run Karoo GP -gp.karoo_gp(tree_type, tree_depth_base, filename) - -