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hierarchical clustering dealing with nans when averaging SI and CH, c… #6

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2 changes: 1 addition & 1 deletion .github/workflows/ci.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ jobs:

strategy:
matrix:
python-version: [3.11, 3.12]
python-version: [3.11]

steps:
- uses: actions/checkout@v3
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44 changes: 33 additions & 11 deletions jale/core/utils/hierarchical.py
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Expand Up @@ -89,8 +89,10 @@ def hierarchical_clustering_pipeline(
project_path=project_path,
meta_name=meta_name,
silhouette_scores=silhouette_scores,
null_silhouette_scores=null_silhouette_scores,
silhouette_scores_z=silhouette_scores_z,
calinski_harabasz_scores=calinski_harabasz_scores,
null_calinski_harabasz_scores=null_calinski_harabasz_scores,
calinski_harabasz_scores_z=calinski_harabasz_scores_z,
exp_separation_density=exp_separation_density,
correlation_type=correlation_type,
Expand Down Expand Up @@ -328,21 +330,21 @@ def compute_hc_metrics_z(
def pooled_std(sample1, sample2):
"""Compute the pooled standard deviation of two samples."""
n1, n2 = sample1.shape[1], sample2.shape[1]
var1, var2 = np.var(sample1, axis=1, ddof=1), np.var(sample2, axis=1, ddof=1)
var1, var2 = np.nanvar(sample1, axis=1, ddof=1), np.nanvar(sample2, axis=1, ddof=1)
return np.sqrt(((n1 - 1) * var1 + (n2 - 1) * var2) / (n1 + n2 - 2))

silhouette_scores_avg = np.average(silhouette_scores, axis=1)
null_silhouette_scores_avg = np.average(null_silhouette_scores, axis=1)
silhouette_scores_avg = np.nanmean(silhouette_scores, axis=1)
null_silhouette_scores_avg = np.nanmean(null_silhouette_scores, axis=1)

if use_pooled_std:
silhouette_std = pooled_std(silhouette_scores, null_silhouette_scores)
else:
silhouette_std = np.std(null_silhouette_scores, axis=1, ddof=1)
silhouette_std = np.nanstd(null_silhouette_scores, axis=1, ddof=1)

silhouette_z = (silhouette_scores_avg - null_silhouette_scores_avg) / silhouette_std

calinski_harabasz_scores_avg = np.average(calinski_harabasz_scores, axis=1)
null_calinski_harabasz_scores_avg = np.average(
calinski_harabasz_scores_avg = np.nanmean(calinski_harabasz_scores, axis=1)
null_calinski_harabasz_scores_avg = np.nanmean(
null_calinski_harabasz_scores, axis=1
)

Expand All @@ -351,7 +353,7 @@ def pooled_std(sample1, sample2):
calinski_harabasz_scores, null_calinski_harabasz_scores
)
else:
calinski_harabasz_std = np.std(null_calinski_harabasz_scores, axis=1, ddof=1)
calinski_harabasz_std = np.nanstd(null_calinski_harabasz_scores, axis=1, ddof=1)

calinski_harabasz_z = (
calinski_harabasz_scores_avg - null_calinski_harabasz_scores_avg
Expand Down Expand Up @@ -435,8 +437,10 @@ def save_hc_metrics(
project_path,
meta_name,
silhouette_scores,
null_silhouette_scores,
silhouette_scores_z,
calinski_harabasz_scores,
null_calinski_harabasz_scores,
calinski_harabasz_scores_z,
exp_separation_density,
correlation_type,
Expand All @@ -446,11 +450,15 @@ def save_hc_metrics(
metrics_df = pd.DataFrame(
{
"Number of Clusters": range(2, max_k + 1),
"Silhouette Scores": np.average(silhouette_scores, axis=1),
"Silhouette Scores SD": np.std(silhouette_scores, axis=1),
"Silhouette Scores": np.nanmean(silhouette_scores, axis=1),
"Silhouette Scores SD": np.nanstd(silhouette_scores, axis=1),
"Silhouette Score Null": np.nanmean(null_silhouette_scores, axis=1),
"Silhouette Scores Null SD": np.nanstd(null_silhouette_scores, axis=1),
"Silhouette Scores Z": silhouette_scores_z,
"Calinski-Harabasz Scores": np.average(calinski_harabasz_scores, axis=1),
"Calinski-Harabasz Scores SD": np.std(calinski_harabasz_scores, axis=1),
"Calinski-Harabasz Scores": np.nanmean(calinski_harabasz_scores, axis=1),
"Calinski-Harabasz Scores Null": np.nanmean(null_calinski_harabasz_scores, axis=1),
"Calinski-Harabasz Scoress Null SD": np.nanstd(null_calinski_harabasz_scores, axis=1),
"Calinski-Harabasz Scores SD": np.nanstd(calinski_harabasz_scores, axis=1),
"Calinski-Harabasz Scores Z": calinski_harabasz_scores_z,
# Pad with NaN for k=2 as metrics start at k=3
"Experiment Separation Density": np.concatenate(
Expand All @@ -471,13 +479,27 @@ def save_hc_metrics(
header=[f"k={k}" for k in range(2, max_k + 1)],
)

pd.DataFrame(null_silhouette_scores.T).to_csv(
project_path
/ f"Results/MA_Clustering/metrics/{meta_name}_Null_silhouette_scores_{correlation_type}_hc_{linkage_method}.csv",
index=False,
header=[f"k={k}" for k in range(2, max_k + 1)],
)

pd.DataFrame(calinski_harabasz_scores.T).to_csv(
project_path
/ f"Results/MA_Clustering/metrics/{meta_name}_calinski_harabasz_scores_{correlation_type}_hc_{linkage_method}.csv",
index=False,
header=[f"k={k}" for k in range(2, max_k + 1)],
)

pd.DataFrame(null_calinski_harabasz_scores.T).to_csv(
project_path
/ f"Results/MA_Clustering/metrics/{meta_name}_Null_calinski_harabasz_scores_{correlation_type}_hc_{linkage_method}.csv",
index=False,
header=[f"k={k}" for k in range(2, max_k + 1)],
)

pd.DataFrame(exp_separation_density.T).to_csv(
project_path
/ f"Results/MA_Clustering/metrics/{meta_name}_exp_separation_density_{correlation_type}_hc_{linkage_method}.csv",
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3 changes: 1 addition & 2 deletions pyproject.toml
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Expand Up @@ -7,13 +7,12 @@ name = "jale"
version = "0.2.9"
description = "Package allowing users to run Activation Likelihood Estimation Meta-Analysis"
readme = "README.md"
requires-python = ">=3.9, <3.13"
requires-python = ">=3.11, <3.12"
license = "MIT"
authors = [{ name = "Lennart Frahm", email = "l.frahm@mailbox.org" }]
classifiers = [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Operating System :: OS Independent",
]
dependencies = [
Expand Down