examples/decoupling_io/components/model_evaluation.py

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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)