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0b2f8bd 604d53b 0b2f8bd aad3f84 0b2f8bd aad3f84 0b2f8bd aad3f84 0b2f8bd 0db9e5f aad3f84 0b2f8bd aad3f84 0b2f8bd aad3f84 0b2f8bd 4cea933 aad3f84 4cea933 72f5b06 | 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 | import joblib
import numpy as np
import os
import sys
backend_dir = os.path.dirname(os.path.abspath(__file__))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
from preprocessing import resolve_model_dir
CANONICAL_CLASSES = ['Asthma', 'Diabetes Mellitus', 'Healthy', 'Heart Disease', 'Hypertension']
class EnsembleModel:
def __init__(self):
self.model_dir = resolve_model_dir()
self.model_names = ['knn', 'svm', 'logreg', 'rf', 'small_nn']
self.models = {}
self.load_models()
self.label_encoder = None
le_path = os.path.join(self.model_dir, 'label_encoder.pkl')
if os.path.exists(le_path):
try:
self.label_encoder = joblib.load(le_path)
except Exception as e:
print(f"Warning: Failed to load label encoder: {e}")
self.weights = {'knn': 0.15, 'svm': 0.15, 'logreg': 0.20, 'rf': 0.25, 'small_nn': 0.25}
def load_models(self):
for name in self.model_names:
path = os.path.join(self.model_dir, f'{name}.pkl')
if os.path.exists(path):
try:
self.models[name] = joblib.load(path)
except Exception as e:
print(f"Warning: Failed to load model {name}: {e}")
def decode_label(self, idx: int) -> str:
"""Safely maps class index to clinical label without throwing on unseen labels."""
if self.label_encoder is not None:
if hasattr(self.label_encoder, 'classes_'):
classes = self.label_encoder.classes_
if 0 <= idx < len(classes):
return str(classes[idx])
if hasattr(self.label_encoder, 'inverse_transform'):
try:
return str(self.label_encoder.inverse_transform([idx])[0])
except Exception:
pass
if 0 <= idx < len(CANONICAL_CLASSES):
return CANONICAL_CLASSES[idx]
return f"Condition_{idx}"
def predict(self, X):
individual_preds = {}
individual_probs = {}
for name, model in self.models.items():
probs = model.predict_proba(X)
individual_probs[name] = probs[0]
pred_indices = int(np.argmax(probs, axis=1)[0])
individual_preds[name] = self.decode_label(pred_indices)
num_classes = list(individual_probs.values())[0].shape[0]
weighted_probs = np.zeros(num_classes)
total_weight = 0
for name in self.models.keys():
w = self.weights.get(name, 1.0)
weighted_probs += individual_probs[name] * w
total_weight += w
weighted_probs /= total_weight
final_pred_idx = int(np.argmax(weighted_probs))
final_pred = self.decode_label(final_pred_idx)
confidence = float(np.max(weighted_probs))
class_probs = {
self.decode_label(i): float(prob)
for i, prob in enumerate(weighted_probs)
}
individual_conf = {
name: float(np.max(probs)) for name, probs in individual_probs.items()
}
return final_pred, confidence, individual_preds, class_probs, self.weights, individual_conf
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