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import joblib
import numpy as np
import sys
import os
# Try to import real classes first
try:
from predict_mental_health import (
RewardModel, VariationalPreferenceLearning, ActiveLearner,
EnhancedQuantumTransform, SuperEnsemble95
)
print("✓ Imported classes from predict_mental_health")
# Hack: If models were saved from a script where these were in __main__,
# we need to alias them in this script's __main__ for joblib to find them.
# Check if we are running as __main__
if __name__ == "__main__":
import sys
current_module = sys.modules[__name__]
setattr(current_module, 'SuperEnsemble95', SuperEnsemble95)
setattr(current_module, 'EnhancedQuantumTransform', EnhancedQuantumTransform)
setattr(current_module, 'RewardModel', RewardModel)
setattr(current_module, 'VariationalPreferenceLearning', VariationalPreferenceLearning)
setattr(current_module, 'ActiveLearner', ActiveLearner)
print("✓ Aliased classes to __main__ for pickle compatibility")
except ImportError as e:
print(f"⚠ Could not import from predict_mental_health: {e}")
# Define dummies if import fails (fallback)
class RewardModel:
def __init__(self, n_features): pass
class VariationalPreferenceLearning:
def __init__(self, n_features, n_latent): pass
class ActiveLearner:
def __init__(self, n_queries=20): pass
class EnhancedQuantumTransform:
def __init__(self, n_features): pass
def transform(self, X): return X
class SuperEnsemble95:
def __init__(self, n_features, n_classes): pass
def inspect_model(name, path):
print(f"\n--- Inspecting {name} ---")
try:
model = joblib.load(path)
print(f"Type: {type(model)}")
if hasattr(model, 'estimators_'):
print(f"Estimators: {len(model.estimators_)}")
if hasattr(model, 'classes_'):
print(f"Classes: {model.classes_}")
return model
except Exception as e:
print(f"Error loading {name}: {e}")
# Detailed debug
import traceback
traceback.print_exc()
return None
def test_determinism(model, scaler, input_data):
print(f"Testing determinism for {model}...")
try:
if scaler:
input_scaled = scaler.transform(input_data)
else:
input_scaled = input_data
preds = []
probas = []
for i in range(5):
p = model.predict(input_scaled)[0]
prob = model.predict_proba(input_scaled)[0]
preds.append(p)
probas.append(prob)
print(f"Predictions (5 runs): {preds}")
# print(f"Probabilities: {probas}")
if len(set(preds)) > 1:
print("!!! WARNING: Model is non-deterministic (Random Output) !!!")
else:
print("Model is deterministic.")
except Exception as e:
print(f"Prediction failed: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
MODEL_DIR = "model"
print("Starting Model Diagnostics...")
# Check if model dir exists
if not os.path.exists(MODEL_DIR):
print(f"Error: {MODEL_DIR} directory not found!")
sys.exit(1)
# 1. Sleep Model
# Input: [age, gender_enc, screen_time, night_screen, social_media, stress]
sleep_model = inspect_model("Sleep Model", f"{MODEL_DIR}/students_sleep_model.joblib")
sleep_scaler = inspect_model("Sleep Scaler", f"{MODEL_DIR}/students_sleep_scaler.joblib")
if sleep_model and sleep_scaler:
test_input = np.array([[20, 0, 6.5, 3.0, 4.5, 22]])
test_determinism(sleep_model, sleep_scaler, test_input)
# 2. Depression Model
# Input: [age, gender, role, social_media, platform, phq9, gad7]
dep_model = inspect_model("Depression Model", f"{MODEL_DIR}/depression_model.joblib")
dep_scaler = inspect_model("Depression Scaler", f"{MODEL_DIR}/depression_scaler.joblib")
if dep_model and dep_scaler:
test_input = np.array([[25, 1, 2, 5.0, 1, 14, 12]])
test_determinism(dep_model, dep_scaler, test_input)
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