def __init__(self, format=None, crit="all"):
self.db = Stats_Database()
self.stats_process = self.db._stats_process
self.crit = crit
self.db_os = Stats_Database(extension="_OS")
self.stats_process_os = self.db_os._stats_process
print self.stats_process_os.count()
self.fontname = "Trebuchet MS"
self.fontsize = 14
self.get_data()
plt.xlabel("Efficiency (one-photon water splitters, " + crit + ")",
fontname=self.fontname,
fontsize=self.fontsize)
plt.ylabel("Efficiency (transparent shields, " + crit + ")",
fontname=self.fontname,
fontsize=self.fontsize)
if format:
plt.savefig("lsos_plot_" + crit + "." + format)
else:
plt.show()
def __init__(self, format=None, criteria="all"):
plt.figure(1, figsize=(16, 8))
self.db = Stats_Database()
self.stats_process = self.db._stats_process
self.num_exps = self.stats_process.count()
self.criteria = criteria
self.fontname = "Trebuchet MS"
params = [
"crossover_fnc", "popsize", "selection_overall", "mutation_rate",
"elitism_num", "fitness_fnc"
]
plt.subplot(3, 2, 1)
for idx, param in enumerate(params):
plt.subplot(3, 2, idx + 1)
self.get_data(param)
if format:
mpl.rcParams['figure.figsize'] = (16, 8)
print mpl.rcParams['figure.figsize']
plt.savefig("parameters_plot." + format)
else:
plt.show()
def __init__(self, format=None):
plt.figure(2)
self.db = Stats_Database()
self.stats_process = self.db._stats_process
self.num_exps = self.stats_process.count()
self.lw = 4
self.fontsize = 16
self.fontname = "Trebuchet MS"
plt.xlabel("GA parameter set rank",
fontname=self.fontname,
fontsize=self.fontsize)
plt.ylabel("Efficiency vs. random",
fontname=self.fontname,
fontsize=self.fontsize)
plt.setp(plt.gca().get_xticklabels(),
fontname=self.fontname,
fontsize=self.fontsize)
plt.setp(plt.gca().get_yticklabels(),
fontname=self.fontname,
fontsize=self.fontsize)
plt.xlim((0, self.num_exps))
self.get_reference()
self.get_data()
if format:
plt.savefig("comparison_plot." + format)
else:
plt.show()
def __init__(self):
self.db = Stats_Database()
self.stats_process = self.db._stats_process
for it in self.stats_process.find():
p = it['parameters']
print("{}\t{}\t{}\t{}\t{}\t{}\t{}").format(
p['popsize'], get_pretty_name(p['selection_fnc']),
get_pretty_name(p['fitness_fnc']),
get_pretty_name(p['crossover_fnc']), p['elitism_num'],
it['ten'], it['all'])
def __init__(self, format=None):
plt.figure(1, figsize=(8, 6))
self.db = Stats_Database()
self.db2 = Stats_Database(extension="_exclusion")
self.stats_process = self.db._stats_process
self.stats_process2 = self.db2._stats_process
self.lw = 4
self.fontsize = 14
self.fontname = "Trebuchet MS"
self.get_goldschmidt_data() # goldschmidt reference
self.get_data(0, "best GA", "dodgerblue", pos="left",
crit="mixed") # best
self.get_data_exclusion(0,
"best GA +\nchemical ",
"#00B31B",
pos="right",
crit="all") # best
self.get_reference_data() # reference
plt.xlabel("Average number of calculations",
fontname=self.fontname,
fontsize=self.fontsize)
plt.ylabel("Potential solar water splitting materials",
fontname=self.fontname,
fontsize=self.fontsize)
plt.setp(plt.gca().get_xticklabels(),
fontname=self.fontname,
fontsize=self.fontsize)
plt.setp(plt.gca().get_yticklabels(),
fontname=self.fontname,
fontsize=self.fontsize)
plt.ylim((0, len(GOOD_CANDS_LS) + 0.5))
plt.xlim((0, 4500))
if format:
plt.savefig("performance_plot_exclusion." + format)
else:
plt.show()
def __init__(self, format=None):
plt.figure(3)
self.db = Stats_Database()
self.stats_process = self.db._stats_process
self.fontname = "Trebuchet MS"
self.fontsize = 14
self.get_data()
plt.xlabel("Efficiency (10 solutions)",
fontname=self.fontname,
fontsize=self.fontsize)
plt.ylabel("Efficiency (all 20 solutions)",
fontname=self.fontname,
fontsize=self.fontsize)
if format:
plt.savefig("tenall_plot." + format)
else:
plt.show()
def __init__(self, format=None):
plt.figure(3)
self.db = Stats_Database()
self.stats_process = self.db._stats_process
self.fontname = "Trebuchet MS"
self.fontsize = 14
self.get_data()
plt.xlabel("Average number of global mutations",
fontname=self.fontname,
fontsize=self.fontsize)
plt.ylabel("Efficiency (all 20 materials)",
fontname=self.fontname,
fontsize=self.fontsize)
if format:
plt.savefig("breakout_plot." + format)
else:
plt.show()
def __init__(self, format=None):
plt.figure(1, figsize=(8, 6))
self.db = Stats_Database()
self.stats_process = self.db._stats_process
num_exps = self.stats_process.count()
self.lw = 4
self.fontsize = 14
self.fontname = "Trebuchet MS"
self.get_reference_data() # reference
# self.get_goldschmidt_data() # goldschmidt reference
self.get_data(0, "best GA", "dodgerblue", pos="right",
crit="mixed") # best
# self.get_data(0, "best GA (ten)", "green", pos="right", crit="ten") # best
#self.get_data(num_exps-1, "worst GA", "tomato", "right") # ~worst
plt.xlabel("Average number of calculations",
fontname=self.fontname,
fontsize=self.fontsize)
plt.ylabel("Potential solar water splitting materials",
fontname=self.fontname,
fontsize=self.fontsize)
plt.setp(plt.gca().get_xticklabels(),
fontname=self.fontname,
fontsize=self.fontsize)
plt.setp(plt.gca().get_yticklabels(),
fontname=self.fontname,
fontsize=self.fontsize)
plt.xticks(np.arange(0, plt.xlim()[1], 2500))
plt.ylim((0, len(GOOD_CANDS_LS) + 0.5))
plt.xlim((0, NUM_CANDS))
if format:
plt.savefig("performance_plot." + format)
else:
plt.show()
def __init__(self, format=None, criteria="all"):
plt.figure(1, figsize=(12, 8))
self.db = Stats_Database()
self.stats_process = self.db._stats_process
self.num_exps = self.stats_process.count()
self.criteria = criteria
self.fontname = "Trebuchet MS"
self.fontsize = 14
params = [
"crossover_fnc", "popsize", "selection_overall", "elitism_num",
"fitness_fnc", "mutation_rate"
]
param_vals = []
for p1 in params:
p1_labels = self.stats_process.distinct("parameters." + p1)
if "-" not in str(p1_labels[0]):
p1_labels.sort()
else:
p1_labels.sort(
key=lambda label: ord(label.split("-")[0][1]) * 10 + float(
label.split("-")[1]) + float(label.split("-")[2]))
for p1x in p1_labels:
param_vals.append((p1, p1x))
values = []
for x1 in param_vals:
for y1 in param_vals:
if x1[0] == y1[0] and x1[1] != y1[1]:
values.append(0.5)
elif x1[0] == y1[0]:
constraint = {"parameters." + x1[0]: x1[1]}
values.append(self.get_score(constraint))
else:
constraint = {
"parameters." + x1[0]: x1[1],
"parameters." + y1[0]: y1[1]
}
values.append(self.get_score(constraint))
values = np.array(values)
values.shape = (len(param_vals), len(param_vals))
plt.hot()
plt.pcolormesh(values)
x = [
str(get_short_name(x[1])) + "=" + get_short_name(x[0])
for x in param_vals
]
ticklok = [z + 0.5 for z in range(0, len(param_vals))]
plt.xticks(ticklok,
x,
ha='center',
rotation=90,
fontsize=14,
fontname=self.fontname)
plt.yticks(ticklok, x, ha='right', fontsize=14, fontname=self.fontname)
last_name = param_vals[0][0]
for idx, label in enumerate(param_vals):
if label[0] != last_name:
plt.plot([idx, idx], [0, len(param_vals)],
color="darkslategray",
linewidth=3)
plt.plot([0, len(param_vals)], [idx, idx],
color="darkslategray",
linewidth=3)
last_name = label[0]
plt.xlim(0, len(param_vals))
plt.ylim(0, len(param_vals))
plt.colorbar()
if format:
plt.savefig("heatmap_plot." + format)
else:
plt.show()
#!/usr/bin/env python
'''
Created on Oct 4, 2012
'''
from ga_optimization_ternary.database import Stats_Database
from ga_optimization_ternary.utils import get_reference_array
__author__ = "Anubhav Jain"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Anubhav Jain"
__email__ = "*****@*****.**"
__date__ = "Oct 4, 2012"
if __name__ == "__main__":
sdb = Stats_Database()
sdb_os = Stats_Database(extension="_OS")
print get_reference_array()[20]
print get_reference_array()[10]
# print headers
params = [
"crossover_fnc", "popsize", "selection_overall", "mutation_rate",
"elitism_num", "fitness_fnc"
]
for param in params:
print param,
print 'ten', 'all', 'half (OS)', 'all (OS)'
for item in sdb._stats_process.find():