def draw_group_all(group_repos, methods, xticks=None, legends=None):
eval_funs = []
if legends is None:
legends = []
for wm in methods:
title = ' + '.join([method_name.get(m, "User-based Jaccard with Time Range") for m, w in wm])
legends.append(title)
print "Processing: " + title
find_similar_repo_func = functools.partial(eval.find_mix, weighted_methods=wm)
eval_f = functools.partial(eval.eval_group_repos, group_repos, find_similar_repo_func)
eval_funs.append(eval_f)
pl, rl, f1 = eval_f()
eval.plot_f1score(f1, pl, rl, "Depth-Score Curve Using " + title)
eval.plot_precision_recall(pl, rl, "Recall-Precision Curve Using " + title)
print "Processing Comparison..."
length = len(methods)
if xticks is None:
xticks = ["Method " + str(i) for i in range(1, length+1)]
# if legends is None:
# legends = ["Jaccard", "Time-aware Jaccard", "User-based LDA",
# "User-based TFIDF", "Text-based LDA", "Text-based TFIDF"]
eval.plot_comparison(eval_funs, xticks, legends,
"Methods Comparison")
def draw_precision_recall():
weighted_methods = [(jaccard_time, 1)]
find_similar_repo_func = functools.partial(eval.find_mix, weighted_methods=weighted_methods)
pl, rl, f1 = eval.eval_single_repo("matplotlib/matplotlib", showcase.sc_data_visualization, find_similar_repo_func)
eval.plot_precision_recall(pl, rl, "Depth-Score Curve Using User-based Jaccard with Time Range")