from pathos.pools import ProcessPool
import Generator
import analysisutil
from Languages import LanguageLoader
from Languages.InformativenessMeasurer import SimMaxInformativenessMeasurer, InformativenessMeasurer
analysisutil.add_argument('inf_strat')
(args, setup, file_util) = analysisutil.init()
languages = LanguageLoader.load_languages(file_util)
universe = Generator.generate_simplified_models(args.model_size)
if args.inf_strat == 'exact':
informativeness_measurer = InformativenessMeasurer(len(universe))
elif args.inf_strat == 'simmax':
informativeness_measurer = SimMaxInformativenessMeasurer(universe)
else:
raise ValueError('{0} is not a valid informativeness strategy.'.format(
args.inf_strat))
with ProcessPool(nodes=args.processes) as pool:
informativeness = pool.map(informativeness_measurer, languages)
file_util.dump_dill(informativeness,
'informativeness_{0}.dill'.format(args.inf_strat))
import analysisutil
import plotnine as pn
from Languages import LanguageLoader
analysisutil.add_argument('complexity_strategy')
analysisutil.add_argument('informativeness_strategy')
analysisutil.add_argument('--include_natural', dest='include_natural_languages', default=False, action='store_true')
(args, setup, file_util) = analysisutil.init()
data = LanguageLoader.load_pandas_table(file_util, args.complexity_strategy, args.informativeness_strategy)
fig = pn.ggplot(data, pn.aes('comm_cost', 'complexity')) +\
pn.geom_point()
#if args.include_natural_languages:
# lex_informativeness = [inf for (ex,inf) in file_util.load_dill('informativeness_{0}_{1}.dill'.format(setup.name, args.informativeness_strategy))]
# lex_complexity = [com for (ex,com) in file_util.load_dill('complexity_{0}_{1}.dill'.format(setup.name, args.complexity_strategy))]
# plt.plot(lex_informativeness, lex_complexity, 'o', color='green')
print(fig)
file_util.save_plotnine(fig, '{0}_{1}_plot'.format(
args.complexity_strategy,
args.informativeness_strategy
))
import analysisutil
from Languages import LanguageLoader
import pandas as pd
import numpy as np
from numpy.linalg import norm
import pygmo
import plotnine as pn
analysisutil.add_argument('table_name')
analysisutil.add_argument('pareto')
analysisutil.add_argument('run_names', nargs='*')
(args, setup, file_util) = analysisutil.init(use_base_dir=True)
pareto_data = LanguageLoader.load_pandas_table(file_util.get_sub_file_util(
args.pareto),
'wordcomplexity',
'simmax',
include_monotonicity=False)
run_df = pd.DataFrame({
'complexity': [],
'comm_cost': [],
'run': [],
'monotonicity': []
})
run_dfs = {}
for run_name in args.run_names:
df = LanguageLoader.load_pandas_table(
file_util.get_sub_file_util(run_name), 'wordcomplexity', 'simmax')
run_dfs[run_name] = df
df['run'] = run_name
import plotnine as pn
import analysisutil
from Languages import LanguageLoader
import statsmodels.formula.api as smf
analysisutil.add_argument('comp_strat')
analysisutil.add_argument('inf_strat')
(args, setup, file_util) = analysisutil.init()
data = LanguageLoader.load_pandas_table(file_util, args.comp_strat, args.inf_strat)
model_inf = smf.ols(formula='monotonicity ~ comm_cost', data=data)
model_comp = smf.ols(formula='monotonicity ~ complexity', data=data)
model_both = smf.ols(formula='monotonicity ~ complexity + comm_cost', data=data)
model = smf.ols(formula='monotonicity ~ complexity * comm_cost', data=data)
result_inf = model_inf.fit()
result_comp = model_comp.fit()
result_both = model_both.fit()
result = model.fit()
print(result_inf.summary())
print(result_comp.summary())
print(result_both.summary())
print(result.summary())
# plt = (pn.ggplot(data, pn.aes('comm_cost', 'complexity'))
# + pn.geom_point()
# + pn.stat_smooth(method='lm'))
# print(plt)
import pygmo
from pathos.multiprocessing import ProcessPool
import Generator
import analysisutil
from Languages import LanguageLoader, LanguageGenerator
from Languages.ComplexityMeasurer import SumComplexityMeasurer
from Languages.InformativenessMeasurer import SimMaxInformativenessMeasurer
analysisutil.add_argument('lang_size', type=int)
analysisutil.add_argument('sample_size', type=int)
analysisutil.add_argument('generations', type=int)
analysisutil.add_argument('-m', '--max_mutations', type=int, default=1)
(args, setup, file_util) = analysisutil.init()
expressions = LanguageLoader.load_all_evaluated_expressions(file_util)
languages = LanguageGenerator.generate_sampled(
expressions, args.lang_size, int(args.sample_size / args.lang_size))
universe = Generator.generate_simplified_models(args.model_size)
def remove(language):
language = copy(language)
index = random.randint(0, len(language) - 1)
language.pop(index)
return language
def add(language):