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measure_diversities.py
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measure_diversities.py
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#
# Measure Diversities
#
# Peter Turney, July 4, 2019
#
# Calculate the standard deviation in the elite
# sample for each generation.
#
import golly as g
import model_classes as mclass
import model_functions as mfunc
import model_parameters as mparam
import numpy as np
import pickle
import statistics
import os
import sys
#
# -----------------------------
# Get some input from the user.
# -----------------------------
#
[pickle_dir, analysis_dir, sorted_pickle_names, \
smallest_pickle_size] = mfunc.choose_pickles(g)
#
# -----------------------------------------------------------------
# Initialize some variables and print them to the output.
# -----------------------------------------------------------------
#
# pickles
#
num_runs = len(sorted_pickle_names)
final_num = smallest_pickle_size
step_size = 1
#
# stats analysis file
#
basename = os.path.basename(os.path.normpath(analysis_dir))
analysis_path = analysis_dir + "/measure-diversities-" + \
basename + ".tsv"
analysis_handle = open(analysis_path, "w", 0)
#
# parameters from model_parameters.py
#
width_factor = mparam.width_factor
height_factor = mparam.height_factor
time_factor = mparam.time_factor
num_trials = mparam.num_trials
#
mfunc.show_message(g, analysis_handle, "\n\nSeed Diversities\n\n")
#
for i in range(num_runs):
message = sorted_pickle_names[i] + "\n"
mfunc.show_message(g, analysis_handle, message)
#
mfunc.show_message(g, analysis_handle, "\n")
#
mfunc.show_message(g, analysis_handle, "\nwidth_factor = " + \
str(width_factor) + "\n")
mfunc.show_message(g, analysis_handle, "height_factor = " + \
str(height_factor) + "\n")
mfunc.show_message(g, analysis_handle, "time_factor = " + \
str(time_factor) + "\n")
mfunc.show_message(g, analysis_handle, "num_trials = " + \
str(num_trials) + "\n\n")
mfunc.show_message(g, analysis_handle, "path = " + \
str(pickle_dir) + "\n\n")
#
# The TSV (tab separated value) file has the format:
#
# <generation number> <tab> <seed density for A>
# <tab> <seed standard deviation for B>
# <tab> <seed standard deviation for C>
# ...
#
# -----------------------------------------------------------------
# For each generation, calculate the elite statistics.
# -----------------------------------------------------------------
#
for i in range(0, final_num + 1, step_size):
# e.g.: i = 0, 1, 2, 3, ..., 75
# fitness for Algorithm 1:
total_fitness = 0
total_sample_size = 0
# for each run in generation i ...
diversities = []
for run in range(num_runs):
pickle_name = sorted_pickle_names[run] # log-2018-11-19-15h-40m-05s
# read in X
x_name = pickle_name + "-pickle-" + str(i) + ".bin"
x_path = pickle_dir + x_name
x_handle = open(x_path, "rb") # rb = read binary
x_sample = pickle.load(x_handle)
x_handle.close()
# calculate the standard deviation of the fitness of the seeds
fitness_list = []
for seed in x_sample:
fitness_list.append(seed.fitness())
standard_deviation = statistics.stdev(fitness_list)
diversities.append("{:.6f}".format(standard_deviation)) # convert to formatted string
# write out the standard deviations
tab = "\t"
mfunc.show_message(g, analysis_handle, \
str(i) + tab + tab.join(diversities) + "\n")
#
# Final message.
#
mfunc.show_message(g, analysis_handle, "\nAnalysis complete.\n")
analysis_handle.close()
#
#