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Merged
sborms merged 2 commits into from
Oct 1, 2021
Merged

issue-#32-improve-PIGs #108

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51e0498
PIG plot fix: correctly aligning long textual bin labels with the bin…
Sep 30, 2021
53a9858
PIG plot fix: avoiding the
Sep 30, 2021
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30 changes: 25 additions & 5 deletions cobra/evaluation/pigs_tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,9 +147,9 @@ def plot_incidence(pig_tables: pd.DataFrame,
with plt.style.context("seaborn-ticks"):
fig, ax = plt.subplots(figsize=dim)

# -----------------
# --------------------------
# Left axis - average target
# -----------------
# --------------------------
ax.plot(df_plot['label'], df_plot['avg_target'],
color="#00ccff", marker=".",
markersize=20, linewidth=3,
Expand All @@ -168,7 +168,9 @@ def plot_incidence(pig_tables: pd.DataFrame,
ax.set_ylabel('incidence' if model_type == "classification" else "mean target value",
fontsize=16)
ax.set_xlabel('{} bins' ''.format(variable), fontsize=16)
ax.xaxis.set_tick_params(rotation=45, labelsize=14)
ax.xaxis.set_tick_params(labelsize=14)
plt.setp(ax.get_xticklabels(),
rotation=45, ha="right", rotation_mode="anchor")
ax.yaxis.set_tick_params(labelsize=14)

if model_type == "classification":
Expand All @@ -177,6 +179,24 @@ def plot_incidence(pig_tables: pd.DataFrame,
ax.set_yticks(np.arange(0, max(df_plot['avg_target'])+0.05, 0.05))
ax.yaxis.set_major_formatter(
FuncFormatter(lambda y, _: '{:.1%}'.format(y)))
elif model_type == "regression":
# If both the difference between the highest avg target of all bins
# versus the global avg target AND the difference between the
# lowest avg target versus the global avg target are both smaller
# than 25% of the global avg target itself, we increase the y
# axis range, to avoid that the minor avg target differences are
# spread out over the configure figure height, suggesting
# incorrectly that there are big differences in avg target across
# the bins and versus the global avg target.
# (Motivation for the AND above: if on one end there IS enough
# difference, the effect that we discuss here does not occur.)
global_avg_target = max(df_plot['global_avg_target']) # series of same number, for every bin.
if (np.abs((max(df_plot['avg_target']) - global_avg_target))
/ global_avg_target < 0.25) \
and (np.abs((min(df_plot['avg_target']) - global_avg_target))
/ global_avg_target < 0.25):
ax.set_ylim(global_avg_target * 0.75,
global_avg_target * 1.25)

# Remove ticks but keep the labels
ax.tick_params(axis='both', which='both', length=0)
Expand All @@ -192,13 +212,13 @@ def plot_incidence(pig_tables: pd.DataFrame,
align='center', color="#939598", zorder=1)

# Set labels & ticks
ax2.set_ylabel('population size', fontsize=16)
ax2.set_xlabel('{} bins' ''.format(variable), fontsize=16)
ax2.xaxis.set_tick_params(rotation=45, labelsize=14)

ax2.yaxis.set_tick_params(labelsize=14)
ax2.yaxis.set_major_formatter(
FuncFormatter(lambda y, _: '{:.1%}'.format(y)))

ax2.set_ylabel('population size', fontsize=16)
ax2.tick_params(axis='y', colors="#939598")
ax2.yaxis.label.set_color('#939598')

Expand Down