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refactor(ml): clean up model_selection.py by removing unused code and improving function structure

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nuluh
2025-07-18 19:27:46 +07:00
parent 18824e05c0
commit f6c71739df
1 changed files with 6 additions and 7 deletions
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13
code/src/ml/model_selection.py
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@@ -8,7 +8,7 @@ from joblib import load
def create_ready_data( def create_ready_data(
stft_data_path: str, stft_data_path: str,
stratify: np.ndarray = None, stratify: np.ndarray = None,
) -> tuple: ) -> tuple[pd.DataFrame, np.ndarray]:
""" """
Create a stratified train-test split from STFT data. Create a stratified train-test split from STFT data.
@@ -22,7 +22,7 @@ def create_ready_data(
Returns: Returns:
-------- --------
tuple tuple
(X_train, X_test, y_train, y_test) - Split datasets (pd.DataFrame, np.ndarray) - Combined data and corresponding labels
""" """
ready_data = [] ready_data = []
for file in os.listdir(stft_data_path): for file in os.listdir(stft_data_path):
@@ -155,7 +155,7 @@ def train_and_evaluate_model(
except Exception as e: except Exception as e:
result["error"] = f"Training error: {str(e)}" result["error"] = f"Training error: {str(e)}"
return result return result
def plot_confusion_matrix(results_sensor, y_test): def plot_confusion_matrix(results_sensor, y_test, title):
""" """
Plot confusion matrices for each model in results_sensor1. Plot confusion matrices for each model in results_sensor1.
@@ -193,8 +193,7 @@ def plot_confusion_matrix(results_sensor, y_test):
# Plot # Plot
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels) disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
disp.plot(cmap=plt.cm.Blues) # You can change colormap disp.plot(cmap=plt.cm.Blues) # You can change colormap
plt.title(f"{i['model']} {i['sensor']} Test") plt.title(f"{title}")
plt.show()
def calculate_label_percentages(labels): def calculate_label_percentages(labels):
""" """
@@ -255,9 +254,9 @@ def inference_model(
fs=1024, fs=1024,
window=hann(1024), window=hann(1024),
nperseg=1024, nperseg=1024,
noverlap=512 noverlap=1024-512
) )
data = pd.DataFrame(np.abs(Zxx).T, columns=[f"Freq_{freq:.2f}" for freq in np.linspace(0, 1024/2, Zxx.shape[1])]) data = pd.DataFrame(np.abs(Zxx).T, columns=[f"Freq_{freq:.2f}" for freq in np.linspace(0, 1024/2, Zxx.shape[1])])
data = data.rename(columns={"Freq_0.00": "00"}) # To match the model input format data = data.rename(columns={"Freq_0.00": "00"}) # To match the model input format
model = load(models) # Load the model from the provided path model = load(models) # Load the model from the provided path
return calculate_label_percentages(model.predict(data)) return calculate_label_percentages(model.predict(data.iloc[:21,:]))