"""
Tests for dsr_feature_eng_ml.evaluation module.
"""

import pytest
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from dsr_feature_eng_ml import evaluation


@pytest.fixture
def sample_data():
    """Create sample data for model evaluation tests."""
    np.random.seed(42)
    n_samples = 100

    return pd.DataFrame(
        {
            "feature_1": np.random.randn(n_samples),
            "feature_2": np.random.randn(n_samples),
            "feature_3": np.random.randn(n_samples),
            "target": np.random.randint(0, 2, n_samples),
        }
    )


class TestEvaluationModule:
    """Test cases for evaluation module."""

    def test_evaluation_module_exists(self):
        """Verify that evaluation module is importable."""
        assert evaluation is not None

    def test_data_splits_creation(self, sample_data):
        """Test DataSplits creation from data source."""
        # Add tests based on your DataSplits implementation
        assert isinstance(sample_data, pd.DataFrame)
        assert "target" in sample_data.columns


class TestModelEvaluationConfig:
    """Test cases for ModelEvaluationConfig class."""

    def test_config_creation(self, sample_data):
        """Test creating a configuration object."""
        # Example test - adjust based on your actual API
        target_col = "target"
        features = [col for col in sample_data.columns if col != target_col]
        assert len(features) > 0

    def test_config_from_dataset(self, sample_data):
        """Test creating config from dataset using factory method."""
        # Add tests based on your from_dataset factory method
        pass


class TestEvaluationResults:
    """Test cases for evaluation results handling."""

    def test_best_model_results_tracking(self):
        """Test best model results tracking functionality."""
        # Add tests based on your BestModelResults class
        pass
