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ENH: Hierarchical clustering of the correlation matrix #19

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ENH: Hierarchical clustering of the correlation matrix #19

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a373e37
enh : implement a new feature to perform hierarchical clustering on t…
celprov Mar 24, 2023
00ea0e8
Apply suggestions from code review to rename flag
celprov Mar 24, 2023
3f10199
sty : simplify the reordering of the correlation matrix
celprov Mar 24, 2023
81867f0
fix: TypeError launched by ax.spines[:]
celprov Oct 25, 2023
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33 changes: 30 additions & 3 deletions mriqc_learn/viz/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,6 +184,7 @@ def plot_corrmat(
cbarlabel="",
symmetric=True,
figsize=None,
sorted=False,
**kwargs,
):
"""
Expand All @@ -204,14 +205,33 @@ def plot_corrmat(
A dictionary with arguments to `matplotlib.Figure.colorbar`. Optional.
cbarlabel
The label for the colorbar. Optional.
sorted : :obj:`bool`
Flag to perform hierachical clustering on the correlation plot
**kwargs
All other arguments are forwarded to `imshow`.

"""
from mpl_toolkits.axes_grid1.inset_locator import inset_axes

# Cluster rows and columns (if arguments enabled)
if sorted:
from scipy.cluster.hierarchy import linkage, dendrogram, fcluster

Z = linkage(data, "complete", optimal_ordering=True)

dendrogram(Z, labels=data.columns, no_plot=True)

# Clusterize the data
threshold = 0.1
labels = fcluster(Z, threshold, criterion="distance")
# Keep the indices to sort labels
labels_order = np.argsort(labels)

# Reorder data
data = data.take(labels_order, axis=0).take(labels_order, axis=1)

if hasattr(data, "columns"):
col_labels = data.columns.tolist()
col_labels = data.columns
data = data.values

if figsize is not None:
Expand All @@ -220,6 +240,7 @@ def plot_corrmat(
if not ax:
ax = plt.gca()

# If matrix is symmetric, keep only lower triangle
if symmetric:
data[np.triu(np.ones(data.shape, dtype=bool))] = np.nan

Expand Down Expand Up @@ -252,10 +273,16 @@ def plot_corrmat(
ax.tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)

# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")
plt.setp(
ax.get_xticklabels(),
rotation=90,
ha="right",
va="center",
rotation_mode="anchor",
)

# Turn spines off and create white grid.
ax.spines[:].set_visible(False)
plt.setp(ax.spines.values(), visible=False)

ax.set_xticks(np.arange(data.shape[1] + 1) - 0.5, minor=True)
ax.set_yticks(np.arange(data.shape[0] + 1) - 0.5, minor=True)
Expand Down
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