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673d9a1 | 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 | # hybrid_model_debugger.py
import pickle
import numpy as np
import sys
import traceback
def debug_model(model_path, test_text):
"""
Debugs the hybrid model by running a detailed test prediction and inspecting the outputs
at each stage of the process
"""
print(f"Loading model from {model_path}...")
try:
# Load model
with open(model_path, "rb") as f:
model_data = pickle.load(f)
print(f"Model loaded successfully. Type: {type(model_data)}")
# Determine the type of model
if isinstance(model_data, dict):
print("\nModel is a dictionary with keys:")
for key in model_data:
print(f" - {key} ({type(model_data[key])})")
# Look for classifier in the dictionary
classifier = None
if 'model' in model_data:
classifier = model_data['model']
print("Using 'model' key as classifier")
elif 'classifier' in model_data:
classifier = model_data['classifier']
print("Using 'classifier' key as classifier")
else:
# Try to find a component with predict method
for key, component in model_data.items():
if hasattr(component, 'predict'):
classifier = component
print(f"Using '{key}' as classifier (has predict method)")
break
else:
# Direct classifier
classifier = model_data
print("Model is a direct classifier object")
if not classifier:
print("ERROR: Could not identify a classifier component in the model")
return
# Check for mlb
mlb = None
if hasattr(classifier, 'mlb'):
mlb = classifier.mlb
print("\nFound MultiLabelBinarizer on classifier")
if hasattr(mlb, 'classes_'):
print(f"Available classes: {mlb.classes_}")
else:
print("WARNING: MultiLabelBinarizer has no classes_ attribute")
else:
print("\nNo MultiLabelBinarizer found on classifier")
# Check if mlb is in the dictionary
if isinstance(model_data, dict) and 'mlb' in model_data:
mlb = model_data['mlb']
print("Found MultiLabelBinarizer in model dictionary")
if hasattr(mlb, 'classes_'):
print(f"Available classes: {mlb.classes_}")
else:
print("WARNING: MultiLabelBinarizer has no classes_ attribute")
# Check for alpha parameter
alpha = getattr(classifier, 'alpha', None)
print(f"\nAlpha parameter: {alpha}")
# Check for threshold parameter
threshold = getattr(classifier, 'threshold', None)
print(f"Threshold parameter: {threshold}")
# Try making a prediction
print(f"\nTesting prediction with text: '{test_text}'")
# Try different prediction approaches
approaches = [
("Standard prediction with text as list", lambda: classifier.predict([test_text])),
("With specific alpha and threshold", lambda: classifier.predict([test_text], alpha=0.6, threshold=0.4)),
("With return_scores=True", lambda: classifier.predict([test_text], return_scores=True)),
("All parameters", lambda: classifier.predict([test_text], alpha=0.6, threshold=0.4, return_scores=True))
]
for description, predict_func in approaches:
print(f"\n--- {description} ---")
try:
result = predict_func()
print(f"Result type: {type(result)}")
print(f"Result value: {result}")
# If it's a numpy array, try to interpret it
if isinstance(result, np.ndarray):
print(f"Array shape: {result.shape}")
print(f"Array contents: {result}")
if mlb and hasattr(mlb, 'classes_'):
try:
# Check if it's a binary array
if len(result.shape) == 2: # First dim is samples, second is classes
labels = mlb.classes_[result[0].astype(bool)].tolist()
print(f"Converted to labels: {labels}")
except Exception as e:
print(f"Error converting to labels: {e}")
# If it's a list, check the first item
elif isinstance(result, list) and len(result) > 0:
print(f"First item type: {type(result[0])}")
print(f"First item value: {result[0]}")
# If it's a dictionary, check its structure
elif isinstance(result, dict):
print("Dictionary keys:")
for key in result:
value = result[key]
print(f" - {key} ({type(value)})")
# Show a sample of the value
if isinstance(value, (list, tuple)) and len(value) > 0:
print(f" Sample: {value[:3]}...")
elif isinstance(value, dict) and len(value) > 0:
sample_keys = list(value.keys())[:3]
print(f" Sample keys: {sample_keys}...")
else:
print(f" Value: {value}")
except Exception as e:
print(f"Error during prediction: {e}")
print(traceback.format_exc())
print("\nDebugging complete")
except Exception as e:
print(f"Error loading or processing model: {e}")
print(traceback.format_exc())
if __name__ == "__main__":
model_path = r"C:\Users\tueyc\CMKL Year 1\nomad_sync_app\backend\hybrid_interest_classifier.pkl"
test_text = "I hike mountains and explore cultures while traveling. I also love cooking new recipes."
if len(sys.argv) > 1:
model_path = sys.argv[1]
if len(sys.argv) > 2:
test_text = sys.argv[2]
debug_model(model_path, test_text) |