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70c7c3b | 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 | from __future__ import annotations
from functools import lru_cache
from pathlib import Path
import pandas as pd
APP_ROOT = Path(__file__).resolve().parent
DATA_DIR = APP_ROOT / "data" / "proc"
PROJECT_ROOT = APP_ROOT.parent
COMPARISON_LOOKUP_PATH = DATA_DIR / "comparison_lookup.parquet"
DISEASE_METADATA_PATH = DATA_DIR / "disease_metadata.csv"
FALLBACK_DL_PATH = PROJECT_ROOT / "Outputs" / "CV_DL" / "oof_dl_preds.parquet"
FALLBACK_TREE_PATH = PROJECT_ROOT / "Outputs" / "CV_tree" / "CB_5_cv.parquet"
FALLBACK_CANDIDATES_PATH = PROJECT_ROOT / "Outputs" / "S2-DL_novel+known_candidates.csv"
COMPARISON_COLUMNS = [
"diseaseId",
"targetId",
"ot_score",
"known_label",
"otrec_oof_pred",
"ottree_pred",
]
DISEASE_META_COLUMNS = [
"diseaseId",
"diseaseName",
"orphan",
"known_clinical_targets",
"comparison_row_count",
"available_ot_score_count",
"available_ottree_count",
]
def _empty_frame(columns: list[str]) -> pd.DataFrame:
return pd.DataFrame(columns=columns)
def _fallback_comparison_available() -> bool:
return FALLBACK_DL_PATH.exists() and FALLBACK_TREE_PATH.exists()
def _validate_comparison_frame(
comparison_df: pd.DataFrame, source: Path
) -> pd.DataFrame:
missing = [
column for column in COMPARISON_COLUMNS if column not in comparison_df.columns
]
if missing:
raise ValueError(
f"comparison lookup at {source} is missing columns: "
f"{', '.join(sorted(missing))}"
)
return comparison_df[COMPARISON_COLUMNS].copy()
@lru_cache(maxsize=1)
def load_comparison_lookup() -> pd.DataFrame:
if COMPARISON_LOOKUP_PATH.exists():
comparison_df = pd.read_parquet(COMPARISON_LOOKUP_PATH)
comparison_df = _validate_comparison_frame(
comparison_df, COMPARISON_LOOKUP_PATH
)
if not comparison_df.empty or not _fallback_comparison_available():
return comparison_df
if not _fallback_comparison_available():
return _empty_frame(COMPARISON_COLUMNS)
otrec = pd.read_parquet(
FALLBACK_DL_PATH,
columns=["diseaseId", "targetId", "score", "label", "pred"],
).rename(
columns={
"score": "ot_score",
"label": "known_label",
"pred": "otrec_oof_pred",
}
)
ottree = pd.read_parquet(
FALLBACK_TREE_PATH,
columns=["diseaseId", "targetId", "pred"],
).rename(columns={"pred": "ottree_pred"})
comparison_df = otrec.merge(ottree, on=["diseaseId", "targetId"], how="left")
return _validate_comparison_frame(comparison_df, FALLBACK_DL_PATH)
@lru_cache(maxsize=1)
def load_disease_metadata() -> pd.DataFrame:
base_metadata = _empty_frame(DISEASE_META_COLUMNS)
if DISEASE_METADATA_PATH.exists():
base_metadata = pd.read_csv(DISEASE_METADATA_PATH)
elif FALLBACK_CANDIDATES_PATH.exists():
candidate_df = pd.read_csv(
FALLBACK_CANDIDATES_PATH,
usecols=[
"diseaseId",
"diseaseName",
"disease_num_known_clinical_targets",
"orphan",
],
).drop_duplicates()
candidate_df = candidate_df.rename(
columns={
"disease_num_known_clinical_targets": "known_clinical_targets",
}
)
base_metadata = candidate_df
comparison_df = load_comparison_lookup()
if comparison_df.empty:
if base_metadata.empty:
return _empty_frame(DISEASE_META_COLUMNS)
for column in DISEASE_META_COLUMNS:
if column not in base_metadata.columns:
base_metadata[column] = pd.NA
return base_metadata[DISEASE_META_COLUMNS].copy()
derived_metadata = (
comparison_df.groupby("diseaseId", as_index=False)
.agg(
known_clinical_targets=("known_label", "sum"),
comparison_row_count=("targetId", "size"),
available_ot_score_count=(
"ot_score",
lambda series: int(series.notna().sum()),
),
available_ottree_count=(
"ottree_pred",
lambda series: int(series.notna().sum()),
),
)
.copy()
)
if base_metadata.empty:
base_metadata = derived_metadata
else:
base_metadata = base_metadata.merge(
derived_metadata, on="diseaseId", how="outer"
)
if "known_clinical_targets_x" in base_metadata.columns:
base_metadata["known_clinical_targets"] = base_metadata[
"known_clinical_targets_x"
].fillna(base_metadata.get("known_clinical_targets_y"))
base_metadata = base_metadata.drop(
columns=[
column
for column in [
"known_clinical_targets_x",
"known_clinical_targets_y",
]
if column in base_metadata.columns
]
)
for column in DISEASE_META_COLUMNS:
if column not in base_metadata.columns:
base_metadata[column] = pd.NA
return base_metadata[DISEASE_META_COLUMNS].copy()
def build_result_annotations(disease_id: str, target_ids: pd.Series) -> pd.DataFrame:
comparison_df = load_comparison_lookup()
if comparison_df.empty:
return _empty_frame(COMPARISON_COLUMNS)
target_ids = pd.Series(target_ids).astype(str)
annotations = comparison_df[
(comparison_df["diseaseId"] == disease_id)
& (comparison_df["targetId"].isin(target_ids.tolist()))
].copy()
return annotations
def get_disease_metadata_row(disease_id: str) -> dict[str, object]:
disease_metadata = load_disease_metadata()
if disease_metadata.empty:
return {}
matches = disease_metadata[disease_metadata["diseaseId"] == disease_id]
if matches.empty:
return {}
return matches.iloc[0].to_dict()
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