File size: 13,607 Bytes
32f5a65 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 | from __future__ import annotations
from dataclasses import dataclass
from typing import Any
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics import silhouette_score
@dataclass(frozen=True)
class MissingnessGroupingResult:
selection_groups: pd.DataFrame
calibration_groups: pd.DataFrame
test_groups: pd.DataFrame
group_labels: dict[int, str]
metadata: dict[str, Any]
def build_missingness_groups(
*,
selection_frame: pd.DataFrame,
calibration_frame: pd.DataFrame,
test_frame: pd.DataFrame,
strategy: str,
candidate_missing_variables: list[str] | None = None,
mask_cluster_k_grid: list[int] | None = None,
min_group_fraction: float = 0.10,
min_selection_group_rows: int = 1,
random_state: int = 0,
**_: Any,
) -> MissingnessGroupingResult:
if strategy == "coverage_gap_variable":
return _build_coverage_gap_variable_groups(
selection_frame=selection_frame,
calibration_frame=calibration_frame,
test_frame=test_frame,
candidate_missing_variables=candidate_missing_variables,
min_group_fraction=min_group_fraction,
min_selection_group_rows=min_selection_group_rows,
)
if strategy == "mask_cluster":
return _build_mask_cluster_groups(
selection_frame=selection_frame,
calibration_frame=calibration_frame,
test_frame=test_frame,
mask_cluster_k_grid=mask_cluster_k_grid or [2, 3, 4, 5],
min_group_fraction=min_group_fraction,
min_selection_group_rows=min_selection_group_rows,
random_state=random_state,
)
raise ValueError(f"unsupported grouping strategy: {strategy}")
def _missing_rate_columns(frame: pd.DataFrame) -> list[str]:
return [
column
for column in frame.columns
if column.endswith("_missing_rate") and column != "global_missing_rate"
]
def _candidate_missing_rate_columns(
frame: pd.DataFrame,
candidate_missing_variables: list[str] | None,
) -> list[str]:
available = set(_missing_rate_columns(frame))
if candidate_missing_variables is None:
return sorted(available)
columns = []
for variable in candidate_missing_variables:
column = f"{variable}_missing_rate"
if column in available:
columns.append(column)
return columns
def _never_observed_indicator(frame: pd.DataFrame, column: str) -> np.ndarray:
values = frame[column].to_numpy(dtype=float)
return values > 0.0
def _frame_groups_from_ids(group_ids: np.ndarray, group_labels: dict[int, str]) -> pd.DataFrame:
return pd.DataFrame(
{
"group": group_ids.astype(int),
"group_label": [group_labels[int(group_id)] for group_id in group_ids],
}
)
def _build_coverage_gap_variable_groups(
*,
selection_frame: pd.DataFrame,
calibration_frame: pd.DataFrame,
test_frame: pd.DataFrame,
candidate_missing_variables: list[str] | None,
min_group_fraction: float,
min_selection_group_rows: int,
) -> MissingnessGroupingResult:
candidate_columns = _candidate_missing_rate_columns(selection_frame, candidate_missing_variables)
diagnostics: dict[str, dict[str, float | int]] = {}
candidate_order = {column: index for index, column in enumerate(candidate_columns)}
def collect_diagnostics(*, relaxed: bool) -> dict[str, dict[str, float | int]]:
local_diagnostics: dict[str, dict[str, float | int]] = {}
for column in candidate_columns:
selection_missing = _never_observed_indicator(selection_frame, column)
calibration_missing = _never_observed_indicator(calibration_frame, column)
selection_fraction = float(selection_missing.mean())
if not relaxed and (
selection_fraction < min_group_fraction
or selection_fraction > (1.0 - min_group_fraction)
):
continue
selection_missing_rows = int(selection_missing.sum())
selection_observed_rows = int((~selection_missing).sum())
calibration_missing_rows = int(calibration_missing.sum())
calibration_observed_rows = int((~calibration_missing).sum())
if not relaxed and min(selection_missing_rows, selection_observed_rows) < min_selection_group_rows:
continue
calibration_fraction = float(calibration_missing.mean())
selection_gap = abs(selection_fraction - calibration_fraction)
imbalance = abs(0.5 - selection_fraction)
local_diagnostics[column] = {
"selection_missing_fraction": selection_fraction,
"calibration_missing_fraction": calibration_fraction,
"selection_missing_rows": selection_missing_rows,
"selection_observed_rows": selection_observed_rows,
"calibration_missing_rows": calibration_missing_rows,
"calibration_observed_rows": calibration_observed_rows,
"selection_gap": selection_gap,
"imbalance": imbalance,
"minority_support": min(
selection_missing_rows,
selection_observed_rows,
),
"relaxed_selection": relaxed,
}
return local_diagnostics
diagnostics = collect_diagnostics(relaxed=False)
if not diagnostics and candidate_missing_variables is None:
diagnostics = collect_diagnostics(relaxed=True)
if not diagnostics:
raise ValueError("no candidates satisfied minimum support and missing-fraction requirements")
for column in candidate_columns:
selection_missing = _never_observed_indicator(selection_frame, column)
calibration_missing = _never_observed_indicator(calibration_frame, column)
selection_fraction = float(selection_missing.mean())
if (
selection_fraction < min_group_fraction
or selection_fraction > (1.0 - min_group_fraction)
):
continue
selection_missing_rows = int(selection_missing.sum())
selection_observed_rows = int((~selection_missing).sum())
calibration_missing_rows = int(calibration_missing.sum())
calibration_observed_rows = int((~calibration_missing).sum())
if min(selection_missing_rows, selection_observed_rows) < min_selection_group_rows:
continue
calibration_fraction = float(calibration_missing.mean())
selection_gap = abs(selection_fraction - calibration_fraction)
imbalance = abs(0.5 - selection_fraction)
if column not in diagnostics:
continue
diagnostics[column].update(
{
"selection_missing_fraction": selection_fraction,
"calibration_missing_fraction": calibration_fraction,
"selection_missing_rows": selection_missing_rows,
"selection_observed_rows": selection_observed_rows,
"calibration_missing_rows": calibration_missing_rows,
"calibration_observed_rows": calibration_observed_rows,
"selection_gap": selection_gap,
"imbalance": imbalance,
"minority_support": min(
selection_missing_rows,
selection_observed_rows,
),
}
)
selected_column = sorted(
diagnostics,
key=lambda column: (
-float(diagnostics[column]["selection_gap"]),
float(diagnostics[column]["imbalance"]),
-int(diagnostics[column]["minority_support"]),
candidate_order.get(column, len(candidate_order)),
),
)[0]
variable = selected_column[: -len("_missing_rate")]
group_labels = {
0: f"{variable.lower()}_ever_observed",
1: f"{variable.lower()}_never_observed",
}
selection_ids = _never_observed_indicator(selection_frame, selected_column).astype(int)
calibration_ids = _never_observed_indicator(calibration_frame, selected_column).astype(int)
test_ids = _never_observed_indicator(test_frame, selected_column).astype(int)
return MissingnessGroupingResult(
selection_groups=_frame_groups_from_ids(selection_ids, group_labels),
calibration_groups=_frame_groups_from_ids(calibration_ids, group_labels),
test_groups=_frame_groups_from_ids(test_ids, group_labels),
group_labels=group_labels,
metadata={
"strategy": "coverage_gap_variable",
"selected_variable": variable,
"group_source": "missing_rate_column",
"group_count": len(group_labels),
"selection_variable_diagnostics": {
column[: -len("_missing_rate")]: values for column, values in diagnostics.items()
},
},
)
def _mask_feature_matrix(frame: pd.DataFrame, columns: list[str]) -> np.ndarray:
return np.column_stack([_never_observed_indicator(frame, column).astype(float) for column in columns])
def _filter_mask_columns(
selection_frame: pd.DataFrame,
columns: list[str],
*,
min_group_fraction: float,
) -> list[str]:
filtered = []
for column in columns:
fraction = float(_never_observed_indicator(selection_frame, column).mean())
if min_group_fraction <= fraction <= (1.0 - min_group_fraction):
filtered.append(column)
return filtered
def _build_mask_cluster_groups(
*,
selection_frame: pd.DataFrame,
calibration_frame: pd.DataFrame,
test_frame: pd.DataFrame,
mask_cluster_k_grid: list[int],
min_group_fraction: float,
min_selection_group_rows: int,
random_state: int,
) -> MissingnessGroupingResult:
columns = _filter_mask_columns(
selection_frame,
_missing_rate_columns(selection_frame),
min_group_fraction=min_group_fraction,
)
if len(columns) < 2:
raise ValueError("mask clustering requires at least two non-constant missingness columns")
selection_mask = _mask_feature_matrix(selection_frame, columns)
calibration_mask = _mask_feature_matrix(calibration_frame, columns)
test_mask = _mask_feature_matrix(test_frame, columns)
n_components = min(10, selection_mask.shape[0], selection_mask.shape[1])
if n_components >= 1 and n_components < selection_mask.shape[1]:
reducer = TruncatedSVD(n_components=n_components, random_state=random_state)
selection_features = reducer.fit_transform(selection_mask)
calibration_features = reducer.transform(calibration_mask)
test_features = reducer.transform(test_mask)
else:
selection_features = selection_mask
calibration_features = calibration_mask
test_features = test_mask
diagnostics: dict[int, dict[str, float | int]] = {}
best_k = None
best_score = None
best_model = None
for k in mask_cluster_k_grid:
if k <= 1 or k > len(selection_features):
diagnostics[k] = {"silhouette": float("nan"), "min_cluster_size": 0, "selection_gap": float("nan")}
continue
model = KMeans(n_clusters=k, random_state=random_state, n_init=10)
selection_ids = model.fit_predict(selection_features)
counts = np.bincount(selection_ids, minlength=k)
min_cluster_size = int(counts.min()) if counts.size else 0
if min_cluster_size < min_selection_group_rows:
diagnostics[k] = {"silhouette": float("nan"), "min_cluster_size": min_cluster_size, "selection_gap": float("nan")}
continue
silhouette = (
float(silhouette_score(selection_features, selection_ids))
if len(np.unique(selection_ids)) > 1
else float("nan")
)
cluster_means = []
for cluster_id in range(k):
cluster_mask = selection_ids == cluster_id
cluster_means.append(float(selection_frame.loc[cluster_mask, "global_missing_rate"].mean()))
selection_gap = float(max(cluster_means) - min(cluster_means)) if cluster_means else 0.0
diagnostics[k] = {
"silhouette": silhouette,
"min_cluster_size": min_cluster_size,
"selection_gap": selection_gap,
}
score = (-k,)
if best_score is None or score > best_score:
best_score = score
best_k = k
best_model = model
if best_model is None or best_k is None:
raise ValueError("mask clustering could not find a feasible k")
selection_ids = best_model.predict(selection_features)
calibration_ids = best_model.predict(calibration_features)
test_ids = best_model.predict(test_features)
group_labels = {group_id: f"cluster_{group_id}" for group_id in range(best_k)}
return MissingnessGroupingResult(
selection_groups=_frame_groups_from_ids(selection_ids, group_labels),
calibration_groups=_frame_groups_from_ids(calibration_ids, group_labels),
test_groups=_frame_groups_from_ids(test_ids, group_labels),
group_labels=group_labels,
metadata={
"strategy": "mask_cluster",
"selected_k": best_k,
"group_source": "missingness_mask_cluster",
"group_count": len(group_labels),
"mask_cluster_diagnostics": diagnostics,
},
)
__all__ = ["MissingnessGroupingResult", "build_missingness_groups"]
|