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import csv
import pickle
from sklearn.preprocessing import StandardScaler
import numpy as np
FEATURE_SCHEMA = [
"num_functions",
"num_loops",
"num_if",
"num_try_except",
"num_return",
"line_count",
"max_nesting_depth",
"cyclomatic_complexity",
"avg_function_length",
"recursion_flag",
"global_variable_count",
]
SCHEMA_VERSION = "v1.0"
SCALER_PATH = os.path.join(os.path.dirname(__file__), "models", "scaler.pkl")
def build_feature_vector(feature_dict):
vector = []
for feature_name in FEATURE_SCHEMA:
value = feature_dict.get(feature_name, 0)
vector.append(float(value))
return vector
def load_dataset(csv_path):
X = []
y = []
with open(csv_path, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
vector = build_feature_vector(row)
label = int(row["label"])
X.append(vector)
y.append(label)
X = np.array(X)
y = np.array(y)
return X, y
def fit_and_save_scaler(X, scaler_path=SCALER_PATH):
scaler = StandardScaler()
scaler.fit(X)
os.makedirs(
os.path.dirname(scaler_path),
exist_ok=True
)
with open(scaler_path, 'wb') as f:
pickle.dump(scaler, f)
print(f"Scaler saved to: {scaler_path}")
return scaler
def load_scaler(scaler_path=SCALER_PATH):
if not os.path.exists(scaler_path):
raise FileNotFoundError(
f"Scaler not found at {scaler_path}. "
f"Run fit_and_save_scaler() first during training."
)
with open(scaler_path, 'rb') as f:
scaler = pickle.load(f)
return scaler
def scale_features(X, scaler):
X = np.array(X)
if X.ndim == 1:
X = X.reshape(1, -1)
X_scaled = scaler.transform(X)
return X_scaled
def prepare_training_data(csv_path, scaler_path=SCALER_PATH):
print("Loading dataset...")
X, y = load_dataset(csv_path)
print(f" Loaded {X.shape[0]} samples, {X.shape[1]} features each")
print("Fitting and saving scaler...")
scaler = fit_and_save_scaler(X, scaler_path)
print("Scaling features...")
X_scaled = scale_features(X, scaler)
print("Training data ready!")
return X_scaled, y
def prepare_inference_vector(feature_dict, scaler_path=SCALER_PATH):
vector = build_feature_vector(feature_dict)
scaler = load_scaler(scaler_path)
X_scaled = scale_features(vector, scaler)
return X_scaled
if __name__ == "__main__":
import os
CSV_PATH = os.path.join(os.path.dirname(__file__), "data", "dataset.csv")
X_scaled, y = prepare_training_data(CSV_PATH)
print("\n" + "=" * 50)
print(" FEATURE PIPELINE — TRAINING RESULTS")
print("=" * 50)
print(f" X_scaled shape : {X_scaled.shape}")
print(f" y shape : {y.shape}")
print(f" Label counts : Clean={sum(y==0)}, Risky={sum(y==1)}")
print(f" Schema version : {SCHEMA_VERSION}")
print(f" Feature order : {FEATURE_SCHEMA}")
print("=" * 50)
print("\n Scaled Feature Means (should be ~0.0 after StandardScaling):")
for name, mean_val in zip(FEATURE_SCHEMA, X_scaled.mean(axis=0)):
print(f" {name:<28} : {mean_val:+.4f}")
print("\n Scaled Feature Std Devs (should be ~1.0 after StandardScaling):")
for name, std_val in zip(FEATURE_SCHEMA, X_scaled.std(axis=0)):
print(f" {name:<28} : {std_val:.4f}")
print("=" * 50)
print("\n Simulating inference on a sample feature dict...")
sample_features = {
"num_functions": 2,
"num_loops": 5,
"num_if": 8,
"num_try_except": 2,
"num_return": 3,
"line_count": 80,
"max_nesting_depth": 6,
"cyclomatic_complexity": 9,
"avg_function_length": 35.0,
"recursion_flag": 1,
"global_variable_count": 2,
}
inference_vector = prepare_inference_vector(sample_features)
print(f" Inference vector shape : {inference_vector.shape}")
print(f" Scaled values : {inference_vector.round(3)}")
print("=" * 50)
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