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669d6a1 | 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 | import sys
import os
sys.path.append(os.getcwd())
import joblib
from pathlib import Path
import __main__
if not hasattr(__main__, "calculate_daily_volatility"):
def calculate_daily_volatility(*args, **kwargs): pass
__main__.calculate_daily_volatility = calculate_daily_volatility
def check_features():
path = Path("Models/Dual_Production")
files = list(path.rglob("*.joblib"))
if not files:
print("No models found")
return
model_data = joblib.load(files[-1])
# Some metadata might contain feature names, or the model itself does
# Let's see if metadata has it
print("Keys in joblib file:", model_data.keys())
# Try to find the training features CSV file
csv_files = list(path.rglob("cv_results.csv"))
if csv_files:
print("Found CSV, checking columns if possible")
# The reports directory also has a training_summary.html with feature importance
# Let's just find ANY dataset file that was saved or check the model directly
model = model_data["model"]
if hasattr(model, "feature_names_in_"):
features = model.feature_names_in_
print(f"Features ({len(features)}):")
for i, f in enumerate(features):
print(f"{i}: {f}")
else:
print("Model doesn't have feature_names_in_")
if __name__ == "__main__":
check_features()
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