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bf8df4f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 | """
Hierarchical rule extraction from XAI attributions.
Two-phase clustering:
Phase 1: cluster instances by which features are in their top-K (Jaccard).
Each Phase-1 cluster represents a "reasoning mode."
Phase 2: within each Phase-1 cluster, re-cluster members in feature-value
space, using the cluster's defining features only. Each Phase-2
sub-cluster represents a "value variant" of that reasoning mode.
The class is XAI-agnostic: it consumes a DataFrame of pre-computed attributions
(index = instance_idx, columns = 'class', 'z0', then one column per feature).
"""
import numpy as np
import pandas as pd
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, fcluster
from sklearn.cluster import KMeans
META_COLS = {"class", "z0"}
class HierarchicalRuleExtractor:
def __init__(
self,
attributions_df, # index = instance_idx; cols: class, z0, + features
reference_data, # numpy (n_rows, n_columns)
feature_specs, # list of dicts (name, type, columns, ...)
K_top=10,
higher_is_better=True,
defining_threshold=0.7,
):
self.attributions = attributions_df.copy()
self.reference_data = np.asarray(reference_data, dtype=np.float32)
self.feature_specs = feature_specs
self._spec_by_name = {spec["name"]: spec for spec in feature_specs}
self.K_top = K_top
self.higher_is_better = higher_is_better
self.defining_threshold = defining_threshold
# Feature columns in the attributions
self.feature_names = [
c for c in self.attributions.columns if c not in META_COLS
]
# Results filled in by fit()
self.phase1_labels = None
self.phase2_labels = None
self.defining_features_per_cluster = None
self.cluster_descriptions = None
# ------------------------------------------------------------
# Phase 1: cluster by top-K feature sets (Jaccard)
# ------------------------------------------------------------
def _build_top_k_matrix(self):
"""Return binary matrix (n_instances, n_features): 1 if feature is top-K."""
n = len(self.attributions)
f = len(self.feature_names)
mat = np.zeros((n, f), dtype=np.int8)
name_to_idx = {name: i for i, name in enumerate(self.feature_names)}
for row_pos, (_, row) in enumerate(self.attributions.iterrows()):
scores = row[self.feature_names].dropna()
ranked = scores.sort_values(ascending=not self.higher_is_better)
top = ranked.index[: self.K_top]
for name in top:
mat[row_pos, name_to_idx[name]] = 1
return mat
def _cluster_phase1(self, top_k_matrix, n_clusters):
"""Hierarchical clustering with Jaccard distance, cut at n_clusters."""
# pdist returns condensed distance vector
dists = pdist(top_k_matrix, metric="jaccard")
Z = linkage(dists, method="average")
labels = fcluster(Z, t=n_clusters, criterion="maxclust")
return labels # 1-indexed cluster labels
# ------------------------------------------------------------
# Defining features per Phase-1 cluster
# ------------------------------------------------------------
def _compute_defining_features(self, top_k_matrix, phase1_labels):
"""For each cluster, list features in top-K for >= threshold of members."""
result = {}
for cid in np.unique(phase1_labels):
members_mask = phase1_labels == cid
cluster_top_k = top_k_matrix[members_mask]
freqs = cluster_top_k.mean(axis=0)
defining_idx = np.where(freqs >= self.defining_threshold)[0]
result[int(cid)] = [self.feature_names[i] for i in defining_idx]
return result
# ------------------------------------------------------------
# Phase 2: cluster on feature values within each Phase-1 cluster
# ------------------------------------------------------------
def _cluster_phase2(self, phase1_labels, defining_features, n_subclusters):
"""
For each Phase-1 cluster, k-means on its members' feature values
(using the cluster's defining features only).
Returns array of sub-cluster labels (one per instance).
"""
n = len(self.attributions)
sub_labels = np.zeros(n, dtype=int)
instance_indices = self.attributions.index.values
for cid in np.unique(phase1_labels):
members_mask = phase1_labels == cid
members_idx_pos = np.where(members_mask)[0]
n_members = len(members_idx_pos)
features = defining_features[int(cid)]
if len(features) == 0 or n_members < 2:
# No defining features or too few members: single sub-cluster
sub_labels[members_idx_pos] = 0
continue
# Get column indices in reference_data for defining features
cols = []
for fname in features:
cols.extend(self._spec_by_name[fname]["columns"])
cols = np.array(cols, dtype=int)
# Pull feature values
row_indices = instance_indices[members_idx_pos]
X_sub = self.reference_data[row_indices][:, cols]
# Number of sub-clusters: user-specified, but capped at members
k = min(n_subclusters, n_members)
if k < 2:
sub_labels[members_idx_pos] = 0
continue
km = KMeans(n_clusters=k, random_state=0, n_init=10)
labels = km.fit_predict(X_sub)
sub_labels[members_idx_pos] = labels
return sub_labels
# ------------------------------------------------------------
# Describe each (phase1, phase2) cluster
# ------------------------------------------------------------
def _describe(self, phase1_labels, phase2_labels, defining_features):
rows = []
instance_indices = self.attributions.index.values
for cid in np.unique(phase1_labels):
members_mask = phase1_labels == cid
features = defining_features[int(cid)]
for sub in np.unique(phase2_labels[members_mask]):
sub_mask = members_mask & (phase2_labels == sub)
row_indices = instance_indices[sub_mask]
sub_classes = self.attributions.loc[row_indices, "class"].values
# Feature value summary for defining features
feature_ranges = {}
for fname in features:
spec = self._spec_by_name[fname]
cols = spec["columns"]
vals = self.reference_data[row_indices][:, cols]
if spec["type"] == "numerical":
v = vals[:, 0]
feature_ranges[fname] = {
"type": "numerical",
"q10": float(np.quantile(v, 0.10)),
"median": float(np.median(v)),
"q90": float(np.quantile(v, 0.90)),
}
else:
# Categorical/ordinal group: report most common active category
active_idx = vals.argmax(axis=1)
most_common = int(np.bincount(active_idx).argmax())
modal_col = cols[most_common]
feature_ranges[fname] = {
"type": spec["type"],
"modal_column": modal_col,
"modal_fraction": float((active_idx == most_common).mean()),
}
rows.append({
"phase1_id": int(cid),
"phase2_id": int(sub),
"n_instances": int(sub_mask.sum()),
"n_TP": int((sub_classes == "TP").sum()),
"n_FP": int((sub_classes == "FP").sum()),
"n_FN": int((sub_classes == "FN").sum()),
"n_TN": int((sub_classes == "TN").sum()),
"defining_features": features,
"feature_ranges": feature_ranges,
})
return pd.DataFrame(rows)
# ------------------------------------------------------------
# Main entry
# ------------------------------------------------------------
def fit(self, n_clusters_phase1=5, n_subclusters_phase2=3):
"""
Run the two-phase clustering.
Parameters
----------
n_clusters_phase1 : int, number of reasoning modes to extract.
n_subclusters_phase2 : int, max number of value variants per mode.
"""
# Phase 1
top_k_matrix = self._build_top_k_matrix()
phase1_labels = self._cluster_phase1(top_k_matrix, n_clusters_phase1)
defining_features = self._compute_defining_features(top_k_matrix, phase1_labels)
# Phase 2
phase2_labels = self._cluster_phase2(phase1_labels, defining_features, n_subclusters_phase2)
# Store
self.phase1_labels = phase1_labels
self.phase2_labels = phase2_labels
self.defining_features_per_cluster = defining_features
self.cluster_descriptions = self._describe(
phase1_labels, phase2_labels, defining_features
)
return self.cluster_descriptions |