mlp_stes += [x[3] for x in mlp_results if x[1] == f[0]]
# lstm_means = [x[2] for x in lstm_results]
# lstm_stes = [x[3] for x in lstm_results]
series = [mlp_means] #, lstm_means]
series_labels = ['MLP'] #, 'LSTM']
series_errs = [mlp_stes] #, lstm_stes]
plot_labels = [f[1] for f in features]
from ss_plotting.make_plots import plot_bar_graph
fig, ax = plot_bar_graph(series,
series_labels=series_labels,
series_errs=series_errs,
series_colors=['grey'],
category_labels=[f[1] for f in features],
category_rotation=45,
xpadding=0.3,
show_plot=False,
simplify=False)
ax.set_ylim([0.70, 0.85])
xlim = ax.get_xlim()
ax.set_xlim([xlim[0], xlim[1] - 0.5])
from ss_plotting import plot_utils
plot_utils.simplify_axis(ax)
plt.savefig("Feature_comparison.eps", format='eps', dpi=1000)
import IPython
IPython.embed()
# ALl, 3F + RoC, 3F/P + RoC, 3F/T + ROC, Z-force + RoC
#!/usr/category/env python
import numpy as np
from ss_plotting.make_plots import plot_bar_graph
import matplotlib.pyplot as plt
# Pretty version of this plot: http://matplotlib.org/examples/api/barchart_demo.html
categories = ['']
proposed_means = [3.31]
proposed_errs = [2.67]
alvar_means = [5.08]
alvar_errs = [2.39]
series = [proposed_means, alvar_means]
series_labels = ['Proposed', 'Alvar']
series_colors = ['red', 'blue']
ylabel = 'Rotation Error (degrees)'
title = 'Average Rotation Errors'
plot_bar_graph(series,
series_colors,
series_labels=series_labels,
series_errs=[proposed_errs, alvar_errs],
category_labels=categories,
plot_ylabel=ylabel,
plot_title=title,
barwidth=0.25,
fontsize=13,
legend_fontsize=13)
xvals = range(1, max_selected_arms+1)
# Now compute the actual value
actual_vals = compute_actuals(arms, args.num_trials,
max_selected_arms = max_selected_arms)
bin_size = 0.1
hist_data, bin_edges = numpy.histogram(arms, bins=numpy.arange(0.0, 1.05, bin_size))
savefile = None
if args.save_plots:
savefile = 'histogram_%s.png' % arm_dist
make_plots.plot_bar_graph([hist_data], [colors[aidx]],
group_color_emphasis = [True],
group_labels = None,
plot_ylabel = 'Counts',
plot_xlabel = 'Success Probability',
bin_labels = bin_edges + 0.5*bin_size,
savefile = savefile,
savefile_size = (.5*4.75, .5*.5*4.75))
# Add expected value data
all_data.append((xvals, expected_vals))
all_labels.append('Expected - %s' % arm_dist)
all_colors.append(colors[aidx])
# all_color_emphasis.append(False)
all_color_emphasis.append(True)
# Add actual value data
# all_data.append((xvals, actual_vals))
# all_labels.append('Actual - %s' % arm_dist)
# all_colors.append(colors[aidx])
#!/usr/category/env python
import numpy as np
from ss_plotting.make_plots import plot_bar_graph
import matplotlib.pyplot as plt
# Pretty version of this plot: http://matplotlib.org/examples/api/barchart_demo.html
categories = ['2 lux', '43 lux', '90 lux', '243 lux']
RGB_means = [0.24, 0.13, 0.03, 0.02]
RGBD_means = [0.03, 0.01, 0.0, 0.0]
series = [RGB_means, RGBD_means]
series_labels = ['RGB', 'RGBD']
series_colors = ['red', 'blue']
ylabel = 'Error Percentage'
title = 'Rotation Error w.r.t. to Lighting'
plot_bar_graph(series, series_colors,
series_labels=series_labels,
category_labels = categories,
plot_ylabel = ylabel,
plot_title = title,
category_padding = 0.45,
fontsize=13,
legend_fontsize=13)
def analyze(datafiles, title=None, out_basename=None):
data = {}
for datafile in datafiles:
with open(datafile, "r") as f:
print "Loading results from file: %s" % datafile
data[datafile] = yaml.load(f)
print "Done"
# Calculate checks per second
groups = []
for datafile in datafiles:
d = data[datafile]
elapsed = float(d["elapsed_ms"]) / 1000.0
checks = int(d["checks"])
checks_per_second = float(checks) / elapsed
groups.append([checks_per_second])
colors = ["purple", "green", "blue", "orange", "red", "pink"]
color_emphasis = [True for c in colors]
group_labels = [name.split("_")[0] for name in datafiles]
# Generate the plot of checks per second
outfile = None
if out_basename is not None:
outfile = "%s.%s.%s" % (out_basename, "cps", "png")
plot_bar_graph(
groups,
group_labels,
colors[: len(groups)],
group_color_emphasis=color_emphasis[: len(groups)],
bin_ticks=False,
plot_ylabel="Checks per second",
plot_title=title,
fontsize=12,
legend_fontsize=12,
savefile=outfile,
savefile_size=(7, 3),
)
if outfile is not None:
logger.info("Saved checks per second data to file %s" % outfile)
# Now generate the plot of average second per check
if out_basename is not None:
outfile = "%s.%s.%s" % (out_basename, "mspc", "png")
new_groups = [[1000.0 * 1.0 / group[0]] for group in groups]
plot_bar_graph(
new_groups,
group_labels,
colors[: len(groups)],
group_color_emphasis=color_emphasis[: len(groups)],
bin_ticks=False,
plot_ylabel="Milliseconds per check",
plot_title=title,
fontsize=12,
legend_fontsize=12,
savefile=outfile,
savefile_size=(7, 3),
)
if outfile is not None:
logger.info("Saved milliseconds per check data to file %s" % outfile)
