Advanced Features

class MultiRegressionCustomMetric:
    """
    Base class for implementing custom metrics for multi-regression tasks.
    
    Allows creation of domain-specific evaluation metrics that can be used
    during training and validation of multi-output regression models.
    """
    
    def __init__(self):
        """Initialize custom metric."""
        pass
    
    def is_max_optimal(self):
        """
        Specify optimization direction for the metric.
        
        Returns:
        bool: True if higher values are better, False if lower values are better
        """
        raise NotImplementedError()
    
    def evaluate(self, approxes, target, weight):
        """
        Calculate metric value for given predictions and targets.
        
        Parameters:
        - approxes: Model predictions (list of numpy.ndarray for each target)
        - target: True target values (numpy.ndarray)
        - weight: Sample weights (numpy.ndarray, optional)
        
        Returns:
        tuple: (metric_value, weight_sum)
            - metric_value: Calculated metric value (float)
            - weight_sum: Sum of weights used (float)
        """
        raise NotImplementedError()
    
    def get_final_error(self, error, weight):
        """
        Calculate final error from accumulated values.
        
        Parameters:
        - error: Accumulated error value (float)
        - weight: Accumulated weight (float)
        
        Returns:
        float: Final metric value
        """
        return error / weight if weight != 0 else 0

class MultiRegressionCustomObjective:
    """
    Base class for implementing custom loss functions (objectives) for multi-regression.
    
    Enables implementation of specialized loss functions tailored to specific
    problem domains or research requirements.
    """
    
    def __init__(self):
        """Initialize custom objective."""
        pass
    
    def calc_ders_range(self, approxes, targets, weights):
        """
        Calculate first and second derivatives of the loss function.
        
        Parameters:
        - approxes: Current model predictions (list of numpy.ndarray)
        - targets: True target values (numpy.ndarray)
        - weights: Sample weights (numpy.ndarray)
        
        Returns:
        tuple: (first_derivatives, second_derivatives)
            - first_derivatives: First derivatives (list of numpy.ndarray)
            - second_derivatives: Second derivatives (list of numpy.ndarray)
        """
        raise NotImplementedError()

# Type aliases for multi-target scenarios
MultiTargetCustomMetric = MultiRegressionCustomMetric
MultiTargetCustomObjective = MultiRegressionCustomObjective

class Tokenizer:
    """
    Text tokenization utility for preprocessing text features in CatBoost.
    
    Provides various tokenization strategies optimized for gradient boosting
    on text data, with support for different languages and text types.
    """
    
    def __init__(self, tokenizer_id='Space', separator_type='ByDelimiter', 
                 delimiter=' ', **kwargs):
        """
        Initialize text tokenizer.
        
        Parameters:
        - tokenizer_id: Tokenizer type ('Space', 'SentenсePiece', 'Regexp')
        - separator_type: How to separate tokens ('ByDelimiter', 'BySeparator')
        - delimiter: Token delimiter character (string)
        - kwargs: Additional tokenizer-specific parameters
        """
        self.tokenizer_id = tokenizer_id
        self.separator_type = separator_type
        self.delimiter = delimiter
    
    def tokenize(self, text):
        """
        Tokenize input text.
        
        Parameters:
        - text: Input text string
        
        Returns:
        list: List of tokens
        """
        # Implementation depends on tokenizer type
        pass

class Dictionary:
    """
    Dictionary builder for text feature processing in CatBoost.
    
    Creates and manages vocabularies for text features, with support for
    frequency-based filtering and domain-specific dictionaries.
    """
    
    def __init__(self, dictionary_id='Word', max_dictionary_size=50000,
                 occurrence_lower_bound=1, **kwargs):
        """
        Initialize dictionary builder.
        
        Parameters:
        - dictionary_id: Dictionary identifier (string)
        - max_dictionary_size: Maximum vocabulary size (int)
        - occurrence_lower_bound: Minimum token frequency (int)
        - kwargs: Additional dictionary parameters
        """
        self.dictionary_id = dictionary_id
        self.max_dictionary_size = max_dictionary_size
        self.occurrence_lower_bound = occurrence_lower_bound
    
    def build(self, texts):
        """
        Build dictionary from text corpus.
        
        Parameters:
        - texts: List of text documents
        
        Returns:
        Dictionary object ready for use in text processing
        """
        pass
    
    def get_dictionary_info(self):
        """
        Get information about the built dictionary.
        
        Returns:
        dict: Dictionary statistics including size and coverage
        """
        pass

def to_polynom(model):
    """
    Convert monoforest model to polynomial representation.
    
    Parameters:
    - model: Trained CatBoost model with monotonic constraints
    
    Returns:
    Polynomial representation of the model that can be used for analysis
    and interpretation of feature relationships.
    """
    pass

def to_polynom_string(model):
    """
    Convert monoforest model to human-readable polynomial string.
    
    Parameters:
    - model: Trained CatBoost model with monotonic constraints
    
    Returns:
    string: Mathematical polynomial expression representing the model
    """
    pass

def explain_features(model):
    """
    Generate feature explanations for monoforest models.
    
    Parameters:
    - model: Trained CatBoost model with monotonic constraints
    
    Returns:
    FeatureExplanation: Detailed explanation of feature contributions
    """
    pass

class FeatureExplanation:
    """
    Container for detailed feature explanations from monoforest models.
    
    Provides structured information about how features contribute to
    predictions in monotonic gradient boosting models.
    """
    
    def __init__(self):
        """Initialize feature explanation container."""
        self.feature_effects = {}
        self.monotonic_constraints = {}
        self.feature_interactions = {}
    
    def get_feature_effect(self, feature_idx):
        """
        Get the effect description for a specific feature.
        
        Parameters:
        - feature_idx: Feature index (int)
        
        Returns:
        dict: Feature effect information including direction and magnitude
        """
        return self.feature_effects.get(feature_idx, {})
    
    def get_monotonic_constraints(self):
        """
        Get monotonic constraints applied to features.
        
        Returns:
        dict: Feature indices mapped to constraint types (1, -1, or 0)
        """
        return self.monotonic_constraints
    
    def summary(self):
        """
        Generate summary of feature explanations.
        
        Returns:
        string: Human-readable summary of model feature behavior
        """
        pass

class CatboostEvaluation:
    """
    Advanced evaluation framework for comprehensive model assessment.
    
    Provides tools for statistical testing, confidence intervals, and
    rigorous comparison of CatBoost models across different scenarios.
    """
    
    def __init__(self, eval_type='Classification', score_type='Logloss'):
        """
        Initialize evaluation framework.
        
        Parameters:
        - eval_type: Type of evaluation ('Classification', 'Regression', 'Ranking')
        - score_type: Primary scoring metric (string)
        """
        self.eval_type = eval_type
        self.score_type = score_type
    
    def add_case(self, case_name, model, test_data, test_labels):
        """
        Add evaluation case to the framework.
        
        Parameters:
        - case_name: Identifier for this evaluation case (string)
        - model: Trained CatBoost model
        - test_data: Test dataset (Pool or array-like)
        - test_labels: True labels (array-like)
        """
        pass
    
    def evaluate(self):
        """
        Perform comprehensive evaluation across all cases.
        
        Returns:
        EvaluationResults: Detailed evaluation results with statistical tests
        """
        pass

class EvaluationResults:
    """Container for comprehensive evaluation results."""
    
    def __init__(self):
        self.case_results = {}
        self.statistical_tests = {}
        self.confidence_intervals = {}
    
    def get_case_result(self, case_name):
        """Get results for specific evaluation case."""
        return self.case_results.get(case_name)
    
    def get_statistical_comparison(self, case1, case2):
        """Get statistical comparison between two cases."""
        pass
    
    def summary_table(self):
        """Generate summary table of all evaluation results."""
        pass

def calc_wilcoxon_test(scores1, scores2, alternative='two-sided'):
    """
    Calculate Wilcoxon signed-rank test for comparing model performance.
    
    Parameters:
    - scores1: Performance scores from first model (array-like)
    - scores2: Performance scores from second model (array-like)
    - alternative: Test alternative ('two-sided', 'less', 'greater')
    
    Returns:
    tuple: (statistic, p_value)
        - statistic: Test statistic (float)
        - p_value: P-value of the test (float)
    """
    pass

def calc_bootstrap_ci_for_mean(scores, confidence_level=0.95, num_bootstrap=10000):
    """
    Calculate bootstrap confidence interval for mean performance.
    
    Parameters:
    - scores: Performance scores (array-like)
    - confidence_level: Confidence level (float, 0-1)
    - num_bootstrap: Number of bootstrap samples (int)
    
    Returns:
    tuple: (lower_bound, upper_bound, mean_estimate)
        - lower_bound: Lower confidence bound (float)
        - upper_bound: Upper confidence bound (float)
        - mean_estimate: Bootstrap mean estimate (float)
    """
    pass

from catboost import CatBoostRegressor, Pool
from catboost import MultiRegressionCustomMetric
import numpy as np

class MeanAbsolutePercentageError(MultiRegressionCustomMetric):
    """Custom MAPE metric implementation."""
    
    def is_max_optimal(self):
        return False  # Lower MAPE is better
    
    def evaluate(self, approxes, target, weight):
        """Calculate MAPE."""
        # approxes[0] contains predictions for single output regression
        predictions = approxes[0]
        
        # Avoid division by zero
        mask = target != 0
        if not np.any(mask):
            return 0.0, len(target)
        
        # Calculate MAPE only for non-zero targets
        ape = np.abs((target[mask] - predictions[mask]) / target[mask]) * 100
        
        if weight is not None:
            weight_sum = np.sum(weight[mask])
            mape = np.sum(ape * weight[mask]) / weight_sum if weight_sum > 0 else 0
        else:
            mape = np.mean(ape)
            weight_sum = len(ape)
        
        return mape, weight_sum

# Use custom metric
custom_mape = MeanAbsolutePercentageError()

model = CatBoostRegressor(
    iterations=200,
    eval_metric=custom_mape,  # Use custom metric for evaluation
    verbose=50
)

model.fit(X_train, y_train, eval_set=[(X_val, y_val)])
print("Model trained with custom MAPE metric")

from catboost import CatBoostClassifier, Pool
from catboost.text_processing import Tokenizer, Dictionary

# Prepare text data
text_data = pd.DataFrame({
    'text_feature': [
        "This is a positive review",
        "Negative sentiment example", 
        "Another positive text sample",
        "Bad negative review text"
    ],
    'category': ['A', 'B', 'A', 'B'],
    'target': [1, 0, 1, 0]
})

# Create pool with text features
text_pool = Pool(
    data=text_data.drop('target', axis=1),
    label=text_data['target'],
    text_features=['text_feature'],
    cat_features=['category']
)

# Configure text processing
text_processing_config = {
    'tokenizers': [
        {
            'tokenizer_id': 'Space',
            'separator_type': 'ByDelimiter',
            'delimiter': ' '
        },
        {
            'tokenizer_id': 'SentencePiece',
            'number_of_tokens': 1000
        }
    ],
    'dictionaries': [
        {
            'dictionary_id': 'Word',
            'max_dictionary_size': 50000,
            'occurrence_lower_bound': 1
        },
        {
            'dictionary_id': 'Bigram', 
            'max_dictionary_size': 50000,
            'occurrence_lower_bound': 2,
            'gram_order': 2
        }
    ],
    'feature_processing': {
        'default': [
            {
                'dictionaries_names': ['Word', 'Bigram'],
                'feature_calcers': ['BoW', 'NaiveBayes'],
                'tokenizers_names': ['Space']
            }
        ]
    }
}

# Train model with text processing
text_model = CatBoostClassifier(
    iterations=100,
    text_processing=text_processing_config,
    verbose=50
)

text_model.fit(text_pool)
print("Text classification model trained")

# Get text feature importance
text_importance = text_model.get_feature_importance(prettified=True)
print("Text feature importance calculated")

from catboost import CatBoostRegressor
from catboost.monoforest import to_polynom_string, explain_features
import numpy as np

# Create synthetic data with known monotonic relationships
np.random.seed(42)
n_samples = 1000

X_mono = pd.DataFrame({
    'increasing_feature': np.random.uniform(0, 10, n_samples),
    'decreasing_feature': np.random.uniform(0, 5, n_samples),
    'neutral_feature': np.random.uniform(-2, 2, n_samples)
})

# Create target with monotonic relationships
y_mono = (
    2 * X_mono['increasing_feature'] +          # Positive monotonic
    -1.5 * X_mono['decreasing_feature'] +       # Negative monotonic  
    0.5 * X_mono['neutral_feature']**2 +        # Non-monotonic
    np.random.normal(0, 0.5, n_samples)         # Noise
)

# Train model with monotonic constraints
mono_model = CatBoostRegressor(
    iterations=200,
    depth=4,
    monotone_constraints=[1, -1, 0],  # +1: increasing, -1: decreasing, 0: no constraint
    verbose=50
)

mono_model.fit(X_mono, y_mono)

# Convert to polynomial representation
try:
    poly_string = to_polynom_string(mono_model)
    print("Polynomial representation:")
    print(poly_string)
except:
    print("Polynomial conversion not available for this model type")

# Get feature explanations
try:
    explanations = explain_features(mono_model)
    print("\nFeature explanations:")
    print(explanations.summary())
except:
    print("Feature explanations not available")

# Verify monotonic behavior
test_values = np.linspace(0, 10, 100)
predictions_increasing = []
predictions_decreasing = []

for val in test_values:
    # Test increasing feature
    test_data_inc = pd.DataFrame({
        'increasing_feature': [val],
        'decreasing_feature': [2.5],  # Fixed value
        'neutral_feature': [0]        # Fixed value  
    })
    pred_inc = mono_model.predict(test_data_inc)[0]
    predictions_increasing.append(pred_inc)
    
    # Test decreasing feature
    test_data_dec = pd.DataFrame({
        'increasing_feature': [5],     # Fixed value
        'decreasing_feature': [val],
        'neutral_feature': [0]        # Fixed value
    })
    pred_dec = mono_model.predict(test_data_dec)[0]
    predictions_decreasing.append(pred_dec)

# Check monotonicity
increasing_diff = np.diff(predictions_increasing) 
decreasing_diff = np.diff(predictions_decreasing)

print(f"\nMonotonic constraint verification:")
print(f"Increasing feature violations: {np.sum(increasing_diff < 0)} / {len(increasing_diff)}")
print(f"Decreasing feature violations: {np.sum(decreasing_diff > 0)} / {len(decreasing_diff)}")

from catboost import CatBoostClassifier
from catboost.eval import calc_wilcoxon_test, calc_bootstrap_ci_for_mean
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score, roc_auc_score
import numpy as np

# Train multiple models for comparison
models = {
    'shallow': CatBoostClassifier(iterations=100, depth=4, verbose=False),
    'medium': CatBoostClassifier(iterations=200, depth=6, verbose=False),
    'deep': CatBoostClassifier(iterations=300, depth=8, verbose=False)
}

# Perform cross-validation for each model
cv_scores = {}
for name, model in models.items():
    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='roc_auc')
    cv_scores[name] = scores
    print(f"{name} model - CV AUC: {scores.mean():.4f} ± {scores.std():.4f}")

# Statistical comparison between models
comparisons = [('shallow', 'medium'), ('medium', 'deep'), ('shallow', 'deep')]

for model1, model2 in comparisons:
    statistic, p_value = calc_wilcoxon_test(
        cv_scores[model1], 
        cv_scores[model2],
        alternative='two-sided'
    )
    
    print(f"\nWilcoxon test: {model1} vs {model2}")
    print(f"Statistic: {statistic:.4f}, P-value: {p_value:.4f}")
    
    if p_value < 0.05:
        better_model = model1 if cv_scores[model1].mean() > cv_scores[model2].mean() else model2
        print(f"Significant difference (p < 0.05): {better_model} performs better")
    else:
        print("No significant difference (p >= 0.05)")

# Bootstrap confidence intervals
for name, scores in cv_scores.items():
    lower, upper, mean_est = calc_bootstrap_ci_for_mean(
        scores, 
        confidence_level=0.95,
        num_bootstrap=10000
    )
    
    print(f"\n{name} model - Bootstrap 95% CI:")
    print(f"Mean: {mean_est:.4f}, CI: [{lower:.4f}, {upper:.4f}]")

# Final model selection and evaluation
best_model_name = max(cv_scores.keys(), key=lambda k: cv_scores[k].mean())
best_model = models[best_model_name]

print(f"\nSelected best model: {best_model_name}")

# Train best model on full training set and evaluate on test set
best_model.fit(X_train, y_train)
test_predictions = best_model.predict_proba(X_test)[:, 1]
test_auc = roc_auc_score(y_test, test_predictions)

print(f"Final test AUC: {test_auc:.4f}")

# Calculate bootstrap CI for test performance
test_bootstrap_scores = []
for _ in range(1000):
    indices = np.random.choice(len(y_test), len(y_test), replace=True)
    boot_auc = roc_auc_score(y_test[indices], test_predictions[indices])
    test_bootstrap_scores.append(boot_auc)

test_lower, test_upper, test_mean = calc_bootstrap_ci_for_mean(
    test_bootstrap_scores,
    confidence_level=0.95
)

print(f"Test AUC 95% Bootstrap CI: [{test_lower:.4f}, {test_upper:.4f}]")