| import numpy as np |
| import pandas as pd |
| from sklearn import base, metrics |
| |
| |
| def y_train_estimation( |
| trained_model: base.ClassifierMixin, train_set: pd.DataFrame, target_column_name: str |
| ) -> np.ndarray: |
| feature_cols = [c for c in train_set.columns if c != target_column_name] |
| return trained_model.predict(train_set[feature_cols]) |
| |
| |
| def y_train(train_set: pd.DataFrame, target_column_name: str) -> pd.Series: |
| return train_set[target_column_name] |
| |
| |
| def cm_train(y_train: pd.Series, y_train_estimation: np.ndarray) -> np.ndarray: |
| return metrics.confusion_matrix(y_train, y_train_estimation) |
| |
| |
| def y_test_estimation( |
| trained_model: base.ClassifierMixin, test_set: pd.DataFrame, target_column_name: str |
| ) -> np.ndarray: |
| feature_cols = [c for c in test_set.columns if c != target_column_name] |
| return trained_model.predict(test_set[feature_cols]) |
| |
| |
| def y_test(test_set: pd.DataFrame, target_column_name: str) -> pd.Series: |
| return test_set[target_column_name] |
| |
| |
| def cm_test(y_test: pd.Series, y_test_estimation: np.ndarray) -> np.ndarray: |
| return metrics.confusion_matrix(y_test, y_test_estimation) |
| |
| |
| def model_predict(trained_model: base.ClassifierMixin, inference_set: pd.DataFrame) -> np.ndarray: |
| return trained_model.predict(inference_set) |
| |
| |
| def confusion_matrix_test_plot(cm_test: np.ndarray) -> metrics.ConfusionMatrixDisplay: |
| return metrics.ConfusionMatrixDisplay(cm_test) |
| |
| |
| def confusion_matrix_training_plot(cm_train: np.ndarray) -> metrics.ConfusionMatrixDisplay: |
| return metrics.ConfusionMatrixDisplay(cm_train) |
