def make_grid_plots(dirname, plot_names=None):
if "depth" in dirname:
# Assume GP
if "depth_6" not in dirname:
ind_names = open(dirname + "/all_trees.dat").read().strip().split("\n")
ind_names = map(lambda x: x.strip(), ind_names)
else:
ind_names = None
elif "ga_length" in dirname:
# Assume GA
length = int(dirname.strip("/").split("_")[2])
ind_names = [bin(i)[2:] for i in range(2**length)]
elif "tsp_length" in dirname:
# Assume TSP
length = int(dirname.strip("/").split("_")[2])
if length < 10:
# 1-digit indices: just concatenate
ind_names = map(lambda t: ''.join(str(ti) for ti in t),
tsp_tours(length))
else:
# use original tuple-style string
ind_names = map(str, tsp_tours(length))
if plot_names is None:
syn_names = syntactic_distance_names(dirname)
grph_names, grph_tex_names = graph_distance_names(dirname)
plot_names = syn_names + grph_names
for plot_name in plot_names:
w = np.genfromtxt(dirname + "/" + plot_name + ".dat")
if "depth_6" not in dirname:
assert(len(w) == len(ind_names))
print plot_name
make_grid(w, False, dirname + "/" + plot_name)
print ind_names # better to print them in a list somewhere than in the graph
def make_grid_plots(dirname, plot_names=None):
if "depth" in dirname:
# Assume GP
if "depth_6" not in dirname:
ind_names = open(dirname +
"/all_trees.dat").read().strip().split("\n")
ind_names = map(lambda x: x.strip(), ind_names)
else:
ind_names = None
elif "ga_length" in dirname:
# Assume GA
length = int(dirname.strip("/").split("_")[2])
ind_names = [bin(i)[2:] for i in range(2**length)]
elif "tsp_length" in dirname:
# Assume TSP
length = int(dirname.strip("/").split("_")[2])
if length < 10:
# 1-digit indices: just concatenate
ind_names = map(lambda t: ''.join(str(ti) for ti in t),
tsp_tours(length))
else:
# use original tuple-style string
ind_names = map(str, tsp_tours(length))
if plot_names is None:
syn_names = syntactic_distance_names(dirname)
grph_names, grph_tex_names = graph_distance_names(dirname)
plot_names = syn_names + grph_names
for plot_name in plot_names:
w = np.genfromtxt(dirname + "/" + plot_name + ".dat")
if "depth_6" not in dirname:
assert (len(w) == len(ind_names))
print plot_name
make_grid(w, False, dirname + "/" + plot_name)
print ind_names # better to print them in a list somewhere than in the graph
def make_SIGEvo_images():
gp_dirname = "../../results/depth_2/"
ga_dirname = "../../results/ga_length_10_per_ind/"
tsp_dirname = "../../results/tsp_length_7_2_opt/"
def clamp(x):
return max(0.0, min(1.0, x))
def colour_val(i):
return clamp(np.log(fit_vals[i]) /
5.5) # NB hardcoded for our fit vals
def marker_size(tree_name):
return 20 + 50 * tree_len(tree_name) / 7.0
def tree_len(tree_name):
return sum(tree_name.count(s) for s in "+-*/xy")
def count_ones(i):
# count ones in the binary rep
return bin(i).count("1")
def tsp_fit(t):
def d(a, b):
return sqrt((a[0] - b[0])**2.0 + (a[1] - b[1])**2.0)
# place a few arbitrary cities
c = [[0, 0], [10, 1], [5, 5], [2, 8], [8, 9], [5, 9], [7, 1]]
s = t + (t[0], ) # replicate initial city to make it easy
return sum(d(c[s[i]], c[s[i + 1]]) for i in range(len(t)))
names = [
"OVD", "FE", "SD_TP", "TED", "TAD1", "FVD", "CT", "SEMD", "Hamming",
"SEMD_alternate_target", "KendallTau"
]
colour_maps = [
new_ocean(), cm.PuRd_r, cm.YlOrRd, cm.copper, cm.autumn, cm.Purples_r,
cm.YlGnBu_r, cm.summer, cm.BuGn_r, cm.afmhot, cm.BuPu_r
]
for name, colour_map in zip(names, colour_maps):
# a hack for doing just a few quickly
if name not in ["SEMD_alternate_target", "KendallTau"]: continue
if name == "Hamming":
# GA
mds_data_filename = ga_dirname + "/" + name + "_MDS.dat"
mds_output_filename = ga_dirname + "/SIGEvo_images/" + name + "_MDS"
colour_vals = [count_ones(i) for i in range(2**10)]
marker_sizes = [30 for i in range(2**10)]
marker = 's' # square
elif name == "KendallTau":
# TSP
mds_data_filename = tsp_dirname + "/" + name + "_MDS.dat"
mds_output_filename = tsp_dirname + "/SIGEvo_images/" + name + "_MDS"
colour_vals = [tsp_fit(t) for t in tsp_tours(7)] # will be scaled
marker_sizes = [30 for i in range(len(colour_vals))]
marker = 'o' # circle
else:
# GP
tree_names = open("../../results/depth_2/all_trees.dat").read(
).strip().split("\n")
if name == "SEMD_alternate_target":
fit_vals = np.genfromtxt(
"../../results/depth_2/all_trees_fitness_alternate_target.dat"
)
else:
fit_vals = np.genfromtxt(
"../../results/depth_2/all_trees_fitness.dat")
mds_data_filename = gp_dirname + "/SEMD_MDS.dat" # not SEMD_alternate_target -- no need
mds_output_filename = gp_dirname + "/SIGEvo_images/" + name + "_MDS"
colour_vals = [colour_val(i) for i in range(len(tree_names))]
marker_sizes = [marker_size(tree_name) for tree_name in tree_names]
marker = '^' # triangle
# get existing MDS data
p = np.genfromtxt(mds_data_filename)
# Make an image
fig, ax = plt.subplots(figsize=(5, 5), frameon=True)
ax.axis('on')
# x- and y-coordinates
ax.set_aspect('equal')
ax.scatter(p[:, 0],
p[:, 1],
s=marker_sizes,
marker=marker,
c=colour_vals,
cmap=colour_map,
alpha=0.5,
edgecolors='none')
fig.savefig(mds_output_filename + ".png", bbox_inches='tight')
fig.savefig(mds_output_filename + ".pdf", bbox_inches='tight')
# elements relevant to a given tour t
def tour_to_idx(t):
ij = zip(t, t[1:] + [t[0]])
return zip(*ij)
# helper function: get the indices needed to set the elements of the
# 'a' matrix
def tour_to_1d_idx(tour, n):
return (i * n + j for i, j in zip(*tour_to_idx(tour)))
C = random_tsp(n)
# calculate cost of each tour
b = np.array([cost(t, x) for t in tsp_tours(n)])
# calculate permuted cost values
b1 = b.copy()
amx, amn = b1.argmax(), b1.argmin()
b1[amx], b1[amn] = b1.min(), b1.max()
# have a look at
for item in tour_to_1d_idx(t, n):
print(item)
# construct a
a = np.zeros((b.shape[0], n * n))
for ti, tour in enumerate(tsp_tours(n)):
a[ti, list(tour_to_1d_idx(tour, n))] = 1
def make_SIGEvo_images():
gp_dirname = "../../results/depth_2/"
ga_dirname = "../../results/ga_length_10_per_ind/"
tsp_dirname = "../../results/tsp_length_7_2_opt/"
def clamp(x):
return max(0.0, min(1.0, x))
def colour_val(i):
return clamp(np.log(fit_vals[i]) / 5.5) # NB hardcoded for our fit vals
def marker_size(tree_name):
return 20 + 50 * tree_len(tree_name) / 7.0
def tree_len(tree_name):
return sum(tree_name.count(s) for s in "+-*/xy")
def count_ones(i):
# count ones in the binary rep
return bin(i).count("1")
def tsp_fit(t):
def d(a, b): return sqrt((a[0]-b[0])**2.0 + (a[1]-b[1])**2.0)
# place a few arbitrary cities
c = [[0, 0],
[10, 1],
[5, 5],
[2, 8],
[8, 9],
[5, 9],
[7, 1]
]
s = t + (t[0],) # replicate initial city to make it easy
return sum(d(c[s[i]], c[s[i+1]]) for i in range(len(t)))
names = ["OVD", "FE", "SD_TP", "TED", "TAD1", "FVD", "CT", "SEMD", "Hamming", "SEMD_alternate_target", "KendallTau"]
colour_maps = [new_ocean(), cm.PuRd_r, cm.YlOrRd, cm.copper, cm.autumn, cm.Purples_r, cm.YlGnBu_r, cm.summer, cm.BuGn_r, cm.afmhot, cm.BuPu_r]
for name, colour_map in zip(names, colour_maps):
# a hack for doing just a few quickly
if name not in ["SEMD_alternate_target", "KendallTau"]: continue
if name == "Hamming":
# GA
mds_data_filename = ga_dirname + "/" + name + "_MDS.dat"
mds_output_filename = ga_dirname + "/SIGEvo_images/" + name + "_MDS"
colour_vals = [count_ones(i) for i in range(2**10)]
marker_sizes = [30 for i in range(2**10)]
marker = 's' # square
elif name == "KendallTau":
# TSP
mds_data_filename = tsp_dirname + "/" + name + "_MDS.dat"
mds_output_filename = tsp_dirname + "/SIGEvo_images/" + name + "_MDS"
colour_vals = [tsp_fit(t) for t in tsp_tours(7)] # will be scaled
marker_sizes = [30 for i in range(len(colour_vals))]
marker = 'o' # circle
else:
# GP
tree_names = open("../../results/depth_2/all_trees.dat").read().strip().split("\n")
if name == "SEMD_alternate_target":
fit_vals = np.genfromtxt("../../results/depth_2/all_trees_fitness_alternate_target.dat")
else:
fit_vals = np.genfromtxt("../../results/depth_2/all_trees_fitness.dat")
mds_data_filename = gp_dirname + "/SEMD_MDS.dat" # not SEMD_alternate_target -- no need
mds_output_filename = gp_dirname + "/SIGEvo_images/" + name + "_MDS"
colour_vals = [colour_val(i) for i in range(len(tree_names))]
marker_sizes = [marker_size(tree_name) for tree_name in tree_names]
marker = '^' # triangle
# get existing MDS data
p = np.genfromtxt(mds_data_filename)
# Make an image
fig, ax = plt.subplots(figsize=(5, 5), frameon=True)
ax.axis('on')
# x- and y-coordinates
ax.set_aspect('equal')
ax.scatter(p[:,0], p[:,1], s=marker_sizes,
marker=marker, c=colour_vals, cmap=colour_map,
alpha=0.5, edgecolors='none')
fig.savefig(mds_output_filename + ".png", bbox_inches='tight')
fig.savefig(mds_output_filename + ".pdf", bbox_inches='tight')
