def plot_stats_function(function,
statss,
paramss,
labels=None,
output_file=None,
limit=0):
"""
function should take a L{pandas.DataFrame} and a dictionary and return a L{pandas.DataFrame}
which will be plotted.
statss and paramss are lists of L{pandas.DataFrame} and L{dict} respectively which will be passed to this function
kwargss is a list of dictionaries, each of which will be passed to the plot function for the respective result of
the function
"""
styles = line_styles(len(statss))
for i, (stats, params, style) in enumerate(zip(statss, paramss, styles)):
if labels:
label = labels[i]
try:
to_plot = function(stats, params)
if limit:
to_plot = to_plot[:limit]
# to_plot.plot(style=style, label=label)
ax = to_plot.plot(label=label, legend=False)
except Exception as e:
print e
patches, labels = ax.get_legend_handles_labels()
# plt.legend(loc='best').get_frame().set_alpha(0.6)
if output_file:
plt.savefig(output_file, bbox_inches='tight')
plt.figure().legend(*ax.get_legend_handles_labels())
def plot_stats_function(function, statss, paramss, labels=None, output_file=None, limit=0):
"""
function should take a L{pandas.DataFrame} and a dictionary and return a L{pandas.DataFrame}
which will be plotted.
statss and paramss are lists of L{pandas.DataFrame} and L{dict} respectively which will be passed to this function
kwargss is a list of dictionaries, each of which will be passed to the plot function for the respective result of
the function
"""
styles = line_styles(len(statss))
for i, (stats, params, style) in enumerate(zip(statss, paramss, styles)):
if labels:
label = labels[i]
try:
to_plot = function(stats, params)
if limit:
to_plot = to_plot[:limit]
# to_plot.plot(style=style, label=label)
ax = to_plot.plot(label=label, legend=False)
except Exception as e:
print e
patches, labels = ax.get_legend_handles_labels()
# plt.legend(loc='best').get_frame().set_alpha(0.6)
if output_file:
plt.savefig(output_file, bbox_inches='tight')
plt.figure().legend(*ax.get_legend_handles_labels())
return model_directory
def paper_names(model_directory):
for name, dir_ in DEFAULT_MODELS.items():
if model_directory == dir_:
return name
relabel = paper_names
stat_example = [s for s in all_stats.values() if len(s) != 0][0]
import matplotlib.pyplot as plt
for stat_name in stat_example:
plt.figure()
plt.title(stat_name)
print len(all_stats)
styles = line_styles(len(all_stats))
for model_directory, style in zip(model_directories, styles):
data = all_stats[model_directory]
try:
to_plot = data[stat_name]
if args.limit:
to_plot = to_plot[to_plot.index <= args.limit]
# ax = to_plot.plot(label=relabel(model_directory), style=style, legend=False, **stat_params[stat_name])
ax = to_plot.plot(label=relabel(model_directory), legend=False, **stat_params[stat_name])
plt.xlabel('training iterations')
plt.ylabel(ys[stat_name])
plt.subplots_adjust(bottom=0.15)
except Exception as e:
print 'exception'
print stat_name, model_directory
print e
