Model Evaluation Framework

class EvalType:
    """
    Enumeration of evaluation types for CatBoost models.
    
    Defines different modes of model evaluation and comparison.
    """

class CatboostEvaluation:
    """
    Main evaluation class for conducting comprehensive model assessments.
    
    Provides infrastructure for running evaluations, collecting metrics,
    and managing evaluation workflows across multiple models and datasets.
    """

class ExecutionCase:
    """
    Represents a single execution case in an evaluation workflow.
    
    Manages the configuration, execution, and results of individual
    evaluation runs within a larger evaluation framework.
    """

class ScoreType:
    """
    Enumeration of score types for evaluation metrics.
    
    Defines different approaches to scoring and metric calculation
    during model evaluation processes.
    """

class ScoreConfig:
    """
    Configuration class for evaluation scoring parameters.
    
    Manages scoring configuration including metric types,
    calculation methods, and evaluation parameters.
    """

class CaseEvaluationResult:
    """
    Results from a single evaluation case.
    
    Contains performance metrics, statistical measures, and
    evaluation outcomes for individual test cases.
    """

class MetricEvaluationResult:
    """
    Results for specific metric evaluations.
    
    Stores detailed results for individual metrics including
    values, confidence intervals, and statistical significance.
    """

class EvaluationResults:
    """
    Container for comprehensive evaluation results.
    
    Aggregates results across multiple cases, metrics, and
    evaluation runs for comprehensive analysis.
    """

def calc_wilcoxon_test():
    """
    Calculate Wilcoxon signed-rank test for paired samples.
    
    Performs non-parametric statistical test to compare
    paired samples and determine statistical significance.
    
    Returns:
    Statistical test results with p-values and significance indicators
    """

def calc_bootstrap_ci_for_mean():
    """
    Calculate bootstrap confidence intervals for mean values.
    
    Uses bootstrap resampling to estimate confidence intervals
    for sample means, providing robust statistical inference.
    
    Returns:
    Confidence interval bounds and bootstrap statistics
    """

def make_dirs_if_not_exists():
    """
    Create directories if they don't exist.
    
    Utility function for managing directory structure
    during evaluation workflows and result storage.
    """

def series_to_line():
    """
    Convert data series to line representation.
    
    Transforms evaluation data series into line format
    for visualization and analysis purposes.
    """

def save_plot():
    """
    Save evaluation plots to files.
    
    Handles saving of evaluation visualizations,
    charts, and plots generated during analysis.
    """

from catboost.eval import CatboostEvaluation, ExecutionCase, ScoreConfig
from catboost import CatBoostClassifier

# Set up evaluation configuration
score_config = ScoreConfig()
evaluation = CatboostEvaluation()

# Create execution case
case = ExecutionCase()

# Configure evaluation parameters
# (Detailed configuration depends on specific evaluation needs)

# Run evaluation
results = evaluation.run_evaluation(case, score_config)

print("Evaluation completed")
print(f"Results: {results}")

from catboost.eval import calc_wilcoxon_test, calc_bootstrap_ci_for_mean

# Perform Wilcoxon test on model comparison results
# (Assumes you have paired performance metrics from two models)
wilcoxon_results = calc_wilcoxon_test()
print(f"Wilcoxon test results: {wilcoxon_results}")

# Calculate bootstrap confidence intervals
bootstrap_ci = calc_bootstrap_ci_for_mean()
print(f"Bootstrap confidence interval: {bootstrap_ci}")

from catboost.eval import make_dirs_if_not_exists, save_plot, series_to_line

# Set up evaluation directory structure
make_dirs_if_not_exists()

# Process evaluation data
line_data = series_to_line()

# Save evaluation visualizations
save_plot()

print("Evaluation workflow completed")

from catboost import CatBoostClassifier, Pool
from catboost.eval import CatboostEvaluation, EvaluationResults

# Train models for comparison
model1 = CatBoostClassifier(iterations=100, depth=4)
model2 = CatBoostClassifier(iterations=200, depth=6)

# Prepare evaluation data
train_pool = Pool(X_train, y_train, cat_features=cat_features)
test_pool = Pool(X_test, y_test, cat_features=cat_features)

# Set up comprehensive evaluation
evaluation = CatboostEvaluation()

# Configure evaluation to compare both models
# (Specific configuration depends on evaluation requirements)

# Execute evaluation
results = evaluation.compare_models(model1, model2, test_pool)

print("Model comparison evaluation completed")