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pref:replacing output file name in classification to enum class
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Haibin committed Aug 9, 2024
1 parent b851a2e commit 36b602a
Showing 1 changed file with 19 additions and 19 deletions.
38 changes: 19 additions & 19 deletions geochemistrypi/data_mining/model/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,24 +144,24 @@ def _classification_report(y_true: pd.DataFrame, y_predict: pd.DataFrame, algori
mlflow.log_artifact(os.path.join(store_path, f"Classification Report - {algorithm_name}.txt"))

@staticmethod
def _cross_validation(trained_model: object, X_train: pd.DataFrame, y_train: pd.DataFrame, average: str, cv_num: int, algorithm_name: str, store_path: str) -> None:
def _cross_validation(trained_model: object, X_train: pd.DataFrame, graph_name: str, y_train: pd.DataFrame, average: str, cv_num: int, algorithm_name: str, store_path: str) -> None:
"""Perform cross validation on the model."""
print("-----* Cross Validation *-----")
print(f"-----* {graph_name} *-----")
print(f"K-Folds: {cv_num}")
scores = cross_validation(trained_model, X_train, y_train, average=average, cv_num=cv_num)
scores_str = json.dumps(scores, indent=4)
save_text(scores_str, f"Cross Validation - {algorithm_name}", store_path)
save_text(scores_str, f"{graph_name} - {algorithm_name}", store_path)

@staticmethod
def _plot_confusion_matrix(y_test: pd.DataFrame, y_test_predict: pd.DataFrame, trained_model: object, algorithm_name: str, local_path: str, mlflow_path: str) -> None:
def _plot_confusion_matrix(y_test: pd.DataFrame, y_test_predict: pd.DataFrame, graph_name: str, trained_model: object, algorithm_name: str, local_path: str, mlflow_path: str) -> None:
"""Plot the confusion matrix of the model."""
print("-----* Confusion Matrix *-----")
print(f"-----* {graph_name} *-----")
data = plot_confusion_matrix(y_test, y_test_predict, trained_model)
save_fig(f"Confusion Matrix - {algorithm_name}", local_path, mlflow_path)
save_fig(f"{graph_name} - {algorithm_name}", local_path, mlflow_path)
index = [f"true_{i}" for i in range(int(y_test.nunique().values))]
columns = [f"pred_{i}" for i in range(int(y_test.nunique().values))]
data = pd.DataFrame(data, columns=columns, index=index)
save_data(data, f"Confusion Matrix - {algorithm_name}", local_path, mlflow_path, True)
save_data(data, f"{graph_name} - {algorithm_name}", local_path, mlflow_path, True)

@staticmethod
def _plot_precision_recall(X_test: pd.DataFrame, y_test: pd.DataFrame, trained_model: object, graph_name: str, algorithm_name: str, local_path: str, mlflow_path: str) -> None:
Expand Down Expand Up @@ -190,27 +190,27 @@ def _plot_precision_recall_threshold(X_test: pd.DataFrame, y_test: pd.DataFrame,
save_data(thresholds, f"{graph_name} - Thresholds", local_path, mlflow_path)

@staticmethod
def _plot_ROC(X_test: pd.DataFrame, y_test: pd.DataFrame, trained_model: object, algorithm_name: str, local_path: str, mlflow_path: str) -> None:
print("-----* ROC Curve *-----")
def _plot_ROC(X_test: pd.DataFrame, y_test: pd.DataFrame, trained_model: object, graph_name: str, algorithm_name: str, local_path: str, mlflow_path: str) -> None:
print(f"-----* {graph_name} *-----")
y_probs, fpr, tpr, thresholds = plot_ROC(X_test, y_test, trained_model, algorithm_name)
save_fig(f"ROC Curve - {algorithm_name}", local_path, mlflow_path)
save_fig(f"{graph_name} - {algorithm_name}", local_path, mlflow_path)
y_probs = pd.DataFrame(y_probs, columns=["Probabilities"])
fpr = pd.DataFrame(fpr, columns=["False Positive Rate"])
tpr = pd.DataFrame(tpr, columns=["True Positive Rate"])
thresholds = pd.DataFrame(thresholds, columns=["Thresholds"])
save_data(y_probs, "ROC Curve - Probabilities", local_path, mlflow_path)
save_data(fpr, "ROC Curve - False Positive Rate", local_path, mlflow_path)
save_data(tpr, "ROC Curve - True Positive Rate", local_path, mlflow_path)
save_data(thresholds, "ROC Curve - Thresholds", local_path, mlflow_path)
save_data(y_probs, f"{graph_name} - Probabilities", local_path, mlflow_path)
save_data(fpr, f"{graph_name} - False Positive Rate", local_path, mlflow_path)
save_data(tpr, f"{graph_name} - True Positive Rate", local_path, mlflow_path)
save_data(thresholds, f"{graph_name} - Thresholds", local_path, mlflow_path)

@staticmethod
def _plot_2d_decision_boundary(X: pd.DataFrame, X_test: pd.DataFrame, trained_model: object, image_config: dict, algorithm_name: str, local_path: str, mlflow_path: str) -> None:
def _plot_2d_decision_boundary(X: pd.DataFrame, X_test: pd.DataFrame, trained_model: object, graph_name: str, image_config: dict, algorithm_name: str, local_path: str, mlflow_path: str) -> None:
"""Plot the decision boundary of the trained model with the testing data set below."""
print("-----* Two-dimensional Decision Boundary Diagram *-----")
print(f"-----* {graph_name} *-----")
plot_2d_decision_boundary(X, X_test, trained_model, image_config)
save_fig(f"Decision Boundary - {algorithm_name}", local_path, mlflow_path)
save_data(X, "Decision Boundary - X", local_path, mlflow_path)
save_data(X_test, "Decision Boundary - X Test", local_path, mlflow_path)
save_fig(f"{graph_name} - {algorithm_name}", local_path, mlflow_path)
save_data(X, f"{graph_name} - X", local_path, mlflow_path)
save_data(X_test, f"{graph_name} - X Test", local_path, mlflow_path)

@staticmethod
def sample_balance(X_train: pd.DataFrame, y_train: pd.DataFrame, local_path: str, mlflow_path: str) -> tuple:
Expand Down

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