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janvanrijn merged 2 commits into from
Oct 11, 2019
Merged

extended #807

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e1b1652
extended
janvanrijn Oct 11, 2019
9041dc6
strang example update
janvanrijn Oct 11, 2019
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24 changes: 22 additions & 2 deletions examples/40_paper/2018_ida_strang_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,10 @@
# adds column that indicates the difference between the two classifiers
evaluations['diff'] = evaluations[flow_ids[0]] - evaluations[flow_ids[1]]


##############################################################################
# makes the s-plot

fig_splot, ax_splot = plt.subplots()
ax_splot.plot(range(len(evaluations)), sorted(evaluations['diff']))
ax_splot.set_title(classifier_family)
Expand All @@ -71,7 +74,10 @@
plt.show()


# adds column that indicates the difference between the two classifiers
##############################################################################
# adds column that indicates the difference between the two classifiers,
# needed for the scatter plot

def determine_class(val_lin, val_nonlin):
if val_lin < val_nonlin:
return class_values[0]
Expand All @@ -84,7 +90,7 @@ def determine_class(val_lin, val_nonlin):
evaluations['class'] = evaluations.apply(
lambda row: determine_class(row[flow_ids[0]], row[flow_ids[1]]), axis=1)

# makes the scatter plot
# does the plotting and formatting
fig_scatter, ax_scatter = plt.subplots()
for class_val in class_values:
df_class = evaluations[evaluations['class'] == class_val]
Expand All @@ -98,3 +104,17 @@ def determine_class(val_lin, val_nonlin):
ax_scatter.set_xscale('log')
ax_scatter.set_yscale('log')
plt.show()

##############################################################################
# makes a scatter plot where each data point represents the performance of the
# two algorithms on various axis (not in the paper)

fig_diagplot, ax_diagplot = plt.subplots()
ax_diagplot.grid(linestyle='--')
ax_diagplot.plot([0, 1], ls="-", color="black")
ax_diagplot.plot([0.2, 1.2], ls="--", color="black")
ax_diagplot.plot([-0.2, 0.8], ls="--", color="black")
ax_diagplot.scatter(evaluations[flow_ids[0]], evaluations[flow_ids[1]])
ax_diagplot.set_xlabel(measure)
ax_diagplot.set_ylabel(measure)
plt.show()