def visualize_correlation_matrix(
self,
corr,
features_list: list,
annot: bool = False,
plot_title: str = None,
ts_title: bool = True,
save_image: bool = False,
output_fp: str = None,
ts_output_fp: bool = False,
show: bool = True,
) -> object:
"""
Graphs df correlation with diagonal matrix. Suitable for lots of
features as it doesn't display annotations. Optionally saves output
to image.
https://seaborn.pydata.org/examples/many_pairwise_correlations.html
"""
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=np.bool))
mask[np.triu_indices_from(mask)] = True
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Draw the heatmap with the mask and correct aspect ratio
hmap = sbn.heatmap(
corr,
annot=annot,
mask=mask,
cmap=cmap,
vmax=1,
vmin=-1,
center=0,
xticklabels=features_list,
yticklabels=features_list,
square=True,
fmt=".2g",
linewidths=0.5,
cbar_kws={"shrink": 0.5},
annot_kws={"size": 6},
)
ts = Misc.make_ts()
plot_title_to_show = self._make_plot_title(ts, plot_title, ts_title)
plt.title(plot_title_to_show)
if save_image:
self._save_image(ts, output_fp, plot_title, ts_output_fp)
if show:
plt.show()
return hmap
