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remove digamma #87

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Merged
NDoering99 merged 1 commit into from
Feb 27, 2025
Merged

remove digamma #87

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83 changes: 21 additions & 62 deletions mdpath/src/mutual_information.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,68 +57,27 @@ def NMI_calcs(self):
for col1 in self.df_all_residues.columns:
for col2 in self.df_all_residues.columns:
if col1 != col2:
if self.digamma_correction:
# Adaptive binning
data_col1 = self.df_all_residues[col1].values
data_col2 = self.df_all_residues[col2].values
hist_col1, bin_edges1 = np.histogram(
data_col1, bins=self.num_bins
)
hist_col2, bin_edges2 = np.histogram(
data_col2, bins=self.num_bins
)
hist_joint, _, _ = np.histogram2d(
data_col1,
data_col2,
bins=(self.num_bins, self.num_bins),
)

# Total data points
total_points = len(data_col1)

# Corrected entropy estimates
entropy_col1 = self.calculate_corrected_entropy(
hist_col1, total_points, self.num_bins
)
entropy_col2 = self.calculate_corrected_entropy(
hist_col2, total_points, self.num_bins
)
joint_entropy = self.calculate_corrected_entropy(
hist_joint.flatten(), total_points, self.num_bins**2
)

# Mutual Information
mi = entropy_col1 + entropy_col2 - joint_entropy
entropys[col1] = entropy_col1
entropys[col2] = entropy_col2

# Normalized MI
nmi = mi / np.sqrt(entropy_col1 * entropy_col2)
normalized_mutual_info[(col1, col2)] = nmi

progress_bar.update(1)
else:
hist_col1, _ = np.histogram(
self.df_all_residues[col1], bins=self.num_bins
)
hist_col2, _ = np.histogram(
self.df_all_residues[col2], bins=self.num_bins
)
hist_joint, _, _ = np.histogram2d(
self.df_all_residues[col1],
self.df_all_residues[col2],
bins=self.num_bins,
)
mi = mutual_info_score(
hist_col1, hist_col2, contingency=hist_joint
)
entropy_col1 = entropy(hist_col1)
entropy_col2 = entropy(hist_col2)
entropys[col1] = entropy_col1
entropys[col2] = entropy_col2
nmi = mi / np.sqrt(entropy_col1 * entropy_col2)
normalized_mutual_info[(col1, col2)] = nmi
progress_bar.update(1)
hist_col1, _ = np.histogram(
self.df_all_residues[col1], bins=self.num_bins
)
hist_col2, _ = np.histogram(
self.df_all_residues[col2], bins=self.num_bins
)
hist_joint, _, _ = np.histogram2d(
self.df_all_residues[col1],
self.df_all_residues[col2],
bins=self.num_bins,
)
mi = mutual_info_score(
hist_col1, hist_col2, contingency=hist_joint
)
entropy_col1 = entropy(hist_col1)
entropy_col2 = entropy(hist_col2)
entropys[col1] = entropy_col1
entropys[col2] = entropy_col2
nmi = mi / np.sqrt(entropy_col1 * entropy_col2)
normalized_mutual_info[(col1, col2)] = nmi
progress_bar.update(1)

entropy_df = pd.DataFrame(entropys.items(), columns=["Residue", "Entropy"])
nmi_df = pd.DataFrame(
Expand Down