def main():
score_mat = get_score_mat()
pd_helper.print_df(score_mat)
x_axis = list(score_mat.index)
average_values = np.zeros(len(x_axis))
for k in score_mat.columns:
y_axis = score_mat[k].values
average_values += y_axis
plt.plot(x_axis, y_axis, '-')
average_values /= len(score_mat.columns)
plt.title("Distance cutoff v performance")
plt.xlabel("Distance cutoff")
plt.ylabel("ratio of program not looked at")
plt.ylim((0.0,1.0))
plt.show()
# Plot the average performance
print("Avg values {0}".format(average_values))
plt.plot(x_axis, average_values, '-')
plt.title("Distance cutoff v avg performance")
plt.xlabel("Distance cutoff")
plt.ylabel("ratio of program not looked at")
plt.ylim((0.0,1.0))
plt.show()
# Plot median performance
median = score_mat.median(axis=1)
plt.plot(x_axis, median, '-')
plt.title("Distance cutoff v median performance")
plt.xlabel("Distance cutoff")
plt.ylabel("ratio of program not looked at")
plt.ylim((0.0,1.0))
plt.show()
def compare_filter(project_name, ranker_type, filter_type, provider_type):
print("Computing with filter")
filter_scores = get_total_scores(project_name, ranker_type, filter_type,
provider_type)
print("Computing without filter")
nofilter_scores = get_total_scores(project_name, "tarantula", "none",
provider_type)
results = pd.DataFrame({'nofilter': nofilter_scores,
'filter': filter_scores,
'diff': filter_scores - nofilter_scores})
results.sort_values('diff', inplace=True)
pd_helper.print_df(results)
filter_bins = get_bin_to_count(filter_scores)
nofilter_bins = get_bin_to_count(nofilter_scores)
bins = pd.DataFrame({'filter': filter_bins,
'nofilter': nofilter_bins,
'diff': filter_bins - nofilter_bins})
pd_helper.print_df(bins)
def print_total_scores(project_name, ranker_type, filter_type, provider_type,
versions=None):
version_to_score = get_total_scores(project_name, ranker_type, filter_type,
provider_type, versions=versions)
print("Average score is {0}".format(version_to_score.mean()))
pd_helper.print_df(version_to_score)
