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0ad96be | 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 | import argparse
import json
import os
import sys
import warnings
from pathlib import Path
# Fix: suppress warnings for cleaner console output
warnings.filterwarnings("ignore")
# Add src to sys.path to allow joblib to resolve custom classes (e.g., TorchMLPClassifier)
SRC_DIR = Path(__file__).parent.absolute()
if SRC_DIR.name != "src":
SRC_DIR = SRC_DIR / "src"
if str(SRC_DIR) not in sys.path:
sys.path.append(str(SRC_DIR))
try:
import joblib
import pandas as pd
import numpy as np
except ImportError:
print("Error: Missing dependencies. Please install: pandas, joblib, scikit-learn, numpy")
sys.exit(1)
# Default configuration from training script
DEFAULT_TARGETS = [
"complications_30d",
"Severe complication",
"KPS_Discharge Worsened",
"New neurological deficits",
]
DEFAULT_MODELS = ["hgb", "rf", "svc", "torch_mlp"]
def print_header(text):
print("\n" + "=" * 60)
print(f" {text}".center(60))
print("=" * 60)
def evaluate():
parser = argparse.ArgumentParser(
description="MedModel Evaluation Utility: Predict multiple targets across multiple models."
)
# Input options
input_group = parser.add_mutually_exclusive_group(required=True)
input_group.add_argument(
"--input",
type=str,
help="Input JSON string. e.g., '{\"Age\": 50, \"Pre-Op KPS\": 80}'",
)
input_group.add_argument(
"--input_file",
type=str,
help="Path to a JSON file containing the input dictionary or list of dictionaries.",
)
# Configuration options
parser.add_argument(
"--targets",
type=str,
default=",".join(DEFAULT_TARGETS),
help="Comma-separated list of target variables.",
)
parser.add_argument(
"--models",
type=str,
default=",".join(DEFAULT_MODELS),
help="Comma-separated list of model names.",
)
parser.add_argument(
"--output_dir",
type=str,
default="./outputs",
help="Base directory for trained model weights.",
)
args = parser.parse_args()
# 1. Load Input Data
try:
if args.input:
raw_data = json.loads(args.input)
else:
with open(args.input_file, "r", encoding="utf-8") as f:
raw_data = json.load(f)
input_list = raw_data if isinstance(raw_data, list) else [raw_data]
df_input = pd.DataFrame(input_list)
except Exception as e:
print(f"Error loading input data: {e}")
sys.exit(1)
targets = [t.strip() for t in args.targets.split(",") if t.strip()]
models = [m.strip() for m in args.models.split(",") if m.strip()]
output_base = Path(args.output_dir)
print_header("MedModel - Multi-Model Prediction")
print(f"Samples: {len(df_input)}")
print(f"Targets: {len(targets)}")
print(f"Models: {len(models)}")
print("-" * 60)
# 2. Iterate over Targets and Models
for target in targets:
print(f"\n[TARGET] {target}")
for model_name in models:
model_path = output_base / target / model_name / "pipeline.joblib"
if not model_path.exists():
print(f" - {model_name:12}: [NOT FOUND] at {model_path}")
continue
try:
# Load pipeline
# Note: src path is in sys.path, so custom classes should resolve
pipeline = joblib.load(model_path)
# Run prediction
preds = pipeline.predict(df_input)
# Check for probabilities
probs = None
if hasattr(pipeline, "predict_proba"):
try:
probs = pipeline.predict_proba(df_input)
except:
pass
# Display results
for i, pred in enumerate(preds):
sample_prefix = f"Sample {i+1} | " if len(df_input) > 1 else ""
confidence_str = ""
if probs is not None:
try:
# Identify the confidence of the predicted class
classes = pipeline.classes_
pred_idx = np.where(classes == pred)[0][0]
conf = probs[i][pred_idx]
confidence_str = f" (Conf: {conf:.1%})"
except:
pass
print(f" - {model_name:12}: {sample_prefix}{pred}{confidence_str}")
except Exception as e:
print(f" - {model_name:12}: [ERROR] {str(e)}")
print("\n" + "=" * 60)
print(" Evaluation Complete.".center(60))
print("=" * 60 + "\n")
if __name__ == "__main__":
evaluate()
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