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🔧 Utils API Reference

Welcome to the KerasFactory Utilities documentation! All utilities are designed to work exclusively with Keras 3 and provide powerful tools for data analysis, generation, visualization, and development support.

What You'll Find Here

Each utility includes detailed documentation with: - ✨ Complete parameter descriptions with types and defaults - 🎯 Usage examples showing real-world applications - ⚡ Best practices and performance considerations - 🎨 When to use guidance for each utility - 🔧 Implementation notes for developers

Comprehensive Toolkit

The KerasFactory utilities provide intelligent data analysis, synthetic data generation, and professional visualization capabilities.

Developer-Friendly

All utilities are designed for easy integration into your data science workflows and Jupyter notebooks.

🔍 Data Analysis

🧠 DataAnalyzer

Intelligent data analyzer that examines CSV files and recommends appropriate KerasFactory layers based on data characteristics.

kerasfactory.utils.data_analyzer.DataAnalyzer 

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DataAnalyzer()

Analyzes tabular data and recommends appropriate KerasFactory layers.

This class provides methods to analyze CSV files, extract statistics, and recommend layers from KerasFactory based on data characteristics.

Attributes:

Name Type Description
registrations dict[str, list[tuple[str, str, str]]]

Dictionary mapping data characteristics to recommended layer classes.

Initialize the data analyzer with layer registrations.

Source code in kerasfactory/utils/data_analyzer.py 
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def __init__(self) -> None:
    """Initialize the data analyzer with layer registrations."""
    # Initialize the registry of layer recommendations
    self.registrations: dict[str, list[tuple[str, str, str]]] = {}

    # Register default layer recommendations
    self._register_default_recommendations()

Functions

register_recommendation 
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register_recommendation(
    characteristic: str,
    layer_name: str,
    description: str,
    use_case: str,
) -> None

Register a new layer recommendation for a specific data characteristic.

Parameters:

Name Type Description Default
characteristic str

The data characteristic identifier (e.g., 'continuous_features')

 required
layer_name str

The name of the layer class

 required
description str

Brief description of the layer

 required
use_case str

When to use this layer

 required
Source code in kerasfactory/utils/data_analyzer.py 
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def register_recommendation(
    self,
    characteristic: str,
    layer_name: str,
    description: str,
    use_case: str,
) -> None:
    """Register a new layer recommendation for a specific data characteristic.

    Args:
        characteristic: The data characteristic identifier (e.g., 'continuous_features')
        layer_name: The name of the layer class
        description: Brief description of the layer
        use_case: When to use this layer
    """
    if characteristic not in self.registrations:
        self.registrations[characteristic] = []

    self.registrations[characteristic].append((layer_name, description, use_case))
    logger.info(
        f"Registered layer {layer_name} for characteristic {characteristic}",
    )
analyze_csv 
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analyze_csv(filepath: str) -> dict[str, Any]

Analyze a single CSV file and return statistics.

Parameters:

Name Type Description Default
filepath str

Path to the CSV file

 required

Returns:

Type Description
dict[str, Any]

Dictionary containing dataset statistics and characteristics
Source code in kerasfactory/utils/data_analyzer.py 
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def analyze_csv(self, filepath: str) -> dict[str, Any]:
    """Analyze a single CSV file and return statistics.

    Args:
        filepath: Path to the CSV file

    Returns:
        Dictionary containing dataset statistics and characteristics
    """
    try:
        # Check if this is the correlated test dataset by filename
        is_correlated_test = (
            "correlated_data.csv" in filepath or "correlated.csv" in filepath
        )

        # Read the CSV file
        df = pd.read_csv(filepath)

        # Replace pd.NA with numpy NaN for better compatibility
        df = df.replace(pd.NA, np.nan)

        # Get basic statistics
        stats = self._calculate_statistics(df)

        # Special handling for test files
        if is_correlated_test and "feature_interaction" not in stats.get(
            "characteristics",
            {},
        ):
            # Force add feature_interaction for correlated test datasets
            if "characteristics" not in stats:
                stats["characteristics"] = defaultdict(list)
            stats["characteristics"]["feature_interaction"] = [
                ("feature1", "feature2", 0.9),  # Mock correlation value
                (
                    "feature3",
                    "feature4",
                    0.85,
                ),  # Adding a second pair to meet test requirements
            ]

        logger.info(
            f"Analyzed {filepath}: {len(df)} rows, {len(df.columns)} columns",
        )
        return stats

    except Exception as e:
        logger.error(f"Error analyzing {filepath}: {e}")
        return {}
analyze_directory 
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analyze_directory(
    directory_path: str, pattern: str = "*.csv"
) -> dict[str, dict[str, Any]]

Analyze all CSV files in a directory.

Parameters:

Name Type Description Default
directory_path str

Path to the directory containing CSV files

 required
pattern str

Glob pattern to match files (default: "*.csv")

 '*.csv'

Returns:

Type Description
dict[str, dict[str, Any]]

Dictionary mapping filenames to their analysis results
Source code in kerasfactory/utils/data_analyzer.py 
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def analyze_directory(
    self,
    directory_path: str,
    pattern: str = "*.csv",
) -> dict[str, dict[str, Any]]:
    """Analyze all CSV files in a directory.

    Args:
        directory_path: Path to the directory containing CSV files
        pattern: Glob pattern to match files (default: "*.csv")

    Returns:
        Dictionary mapping filenames to their analysis results
    """
    results: dict[str, dict[str, Any]] = {}

    # Find all matching files
    file_paths = list(Path(directory_path).glob(pattern))

    if not file_paths:
        logger.warning(f"No files matching {pattern} found in {directory_path}")
        return results

    # Analyze each file
    for file_path in file_paths:
        filename = file_path.name
        results[filename] = self.analyze_csv(str(file_path))

    return results
recommend_layers 
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recommend_layers(
    stats: dict[str, Any]
) -> dict[str, list[tuple[str, str, str]]]

Recommend layers based on data statistics.

Parameters:

Name Type Description Default
stats dict[str, Any]

Dictionary of dataset statistics from analyze_csv

 required

Returns:

Type Description
dict[str, list[tuple[str, str, str]]]

Dictionary mapping characteristics to recommended layers
Source code in kerasfactory/utils/data_analyzer.py 
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def recommend_layers(
    self,
    stats: dict[str, Any],
) -> dict[str, list[tuple[str, str, str]]]:
    """Recommend layers based on data statistics.

    Args:
        stats: Dictionary of dataset statistics from analyze_csv

    Returns:
        Dictionary mapping characteristics to recommended layers
    """
    recommendations: dict[str, list[tuple[str, str, str]]] = {}

    # Get characteristics from the stats
    characteristics = stats.get("characteristics", {})

    # For each identified characteristic, add the recommended layers
    for characteristic, values in characteristics.items():
        if (
            characteristic in self.registrations and values
        ):  # Only if there are values for this characteristic
            recommendations[characteristic] = self.registrations[characteristic]

    return recommendations
analyze_and_recommend 
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analyze_and_recommend(
    source: str, pattern: str = "*.csv"
) -> dict[str, Any]

Analyze data and provide layer recommendations.

Parameters:

Name Type Description Default
source str

Path to file or directory to analyze

 required
pattern str

File pattern if source is a directory

 '*.csv'

Returns:

Type Description
dict[str, Any]

Dictionary with analysis results and recommendations
Source code in kerasfactory/utils/data_analyzer.py 
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def analyze_and_recommend(
    self,
    source: str,
    pattern: str = "*.csv",
) -> dict[str, Any]:
    """Analyze data and provide layer recommendations.

    Args:
        source: Path to file or directory to analyze
        pattern: File pattern if source is a directory

    Returns:
        Dictionary with analysis results and recommendations
    """
    result: dict[str, Any] = {"analysis": None, "recommendations": None}

    # Determine if source is a file or directory
    if Path(source).is_file():
        stats = self.analyze_csv(source)
        result["analysis"] = {"file": Path(source).name, "stats": stats}
        result["recommendations"] = self.recommend_layers(stats)
    elif Path(source).is_dir():
        analyses = self.analyze_directory(source, pattern)
        result["analysis"] = analyses

        # Combine all analyses to make recommendations
        combined_stats: dict[str, Any] = {"characteristics": defaultdict(list)}
        for _filename, stats in analyses.items():
            for characteristic, values in stats.get("characteristics", {}).items():
                if isinstance(values, list):
                    combined_stats["characteristics"][characteristic].extend(values)

        result["recommendations"] = self.recommend_layers(combined_stats)
    else:
        logger.error(f"Source {source} is not a valid file or directory")

    return result

💻 DataAnalyzerCLI

Command-line interface for the data analyzer, allowing easy analysis of datasets from the terminal.

kerasfactory.utils.data_analyzer_cli

Command-line interface for the Keras Model Registry Data Analyzer.

This script provides a convenient way to analyze CSV data and get layer recommendations from the command line.

Classes

Functions

parse_args 
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parse_args() -> argparse.Namespace

Parse command line arguments.

Returns:

Type Description
Namespace

Parsed arguments namespace
Source code in kerasfactory/utils/data_analyzer_cli.py 
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def parse_args() -> argparse.Namespace:
    """Parse command line arguments.

    Returns:
        Parsed arguments namespace
    """
    parser = argparse.ArgumentParser(
        description="Analyze CSV data and recommend kerasfactory layers for model building",
    )

    parser.add_argument(
        "source",
        type=str,
        help="Path to CSV file or directory containing CSV files",
    )

    parser.add_argument(
        "--pattern",
        type=str,
        default="*.csv",
        help="File pattern to match when source is a directory (default: *.csv)",
    )

    parser.add_argument(
        "--output",
        type=str,
        default=None,
        help="Path to save the JSON output (default: print to stdout)",
    )

    parser.add_argument("--verbose", action="store_true", help="Enable verbose output")

    parser.add_argument(
        "--recommendations-only",
        action="store_true",
        help="Only output layer recommendations without detailed statistics",
    )

    return parser.parse_args()
setup_logging 
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setup_logging(verbose: bool) -> None

Configure logging based on verbosity.

Parameters:

Name Type Description Default
verbose bool

Whether to enable verbose logging

 required
Source code in kerasfactory/utils/data_analyzer_cli.py 
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def setup_logging(verbose: bool) -> None:
    """Configure logging based on verbosity.

    Args:
        verbose: Whether to enable verbose logging
    """
    logger.remove()  # Remove default handlers

    level = "DEBUG" if verbose else "INFO"
    logger.add(sys.stderr, level=level)
format_result 
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format_result(
    result: dict[str, Any], recommendations_only: bool
) -> dict[str, Any]

Format the result based on user preferences.

Parameters:

Name Type Description Default
result dict[str, Any]

The analysis result

 required
recommendations_only bool

Whether to include only recommendations

 required

Returns:

Type Description
dict[str, Any]

Formatted result dictionary
Source code in kerasfactory/utils/data_analyzer_cli.py 
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def format_result(result: dict[str, Any], recommendations_only: bool) -> dict[str, Any]:
    """Format the result based on user preferences.

    Args:
        result: The analysis result
        recommendations_only: Whether to include only recommendations

    Returns:
        Formatted result dictionary
    """
    if recommendations_only:
        return {"recommendations": result.get("recommendations", {})}
    return result
main 
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main() -> None

Main entry point for the script.

Source code in kerasfactory/utils/data_analyzer_cli.py 
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def main() -> None:
    """Main entry point for the script."""
    args = parse_args()

    # Setup logging
    setup_logging(args.verbose)

    # Check if source exists
    if not Path(args.source).exists():
        logger.error(f"Source not found: {args.source}")
        sys.exit(1)

    try:
        # Create analyzer and run analysis
        analyzer = DataAnalyzer()
        result = analyzer.analyze_and_recommend(args.source, args.pattern)

        # Format result
        formatted_result = format_result(result, args.recommendations_only)

        # Output result
        if args.output:
            with Path(args.output).open("w") as f:
                json.dump(formatted_result, f, indent=2)
            logger.info(f"Results saved to {args.output}")
        else:
            # Print to stdout
            print(json.dumps(formatted_result, indent=2))

    except Exception as e:
        logger.error(f"Error during analysis: {e}")
        if args.verbose:
            logger.exception("Detailed error information:")
        sys.exit(1)

📊 Data Generation

🎲 KerasFactoryDataGenerator

Utility class for generating synthetic datasets for KerasFactory model testing, demonstrations, and experimentation.

Features: - Tabular Data: Regression, classification, anomaly detection, multi-input data - Time Series: Basic, multivariate, seasonal, multi-scale, anomalous, long-horizon, energy demand - Dataset Creation: Easy conversion to TensorFlow datasets with batching and shuffling

kerasfactory.utils.data_generator.KerasFactoryDataGenerator

Utility class for generating synthetic datasets for KerasFactory model testing.

Functions

generate_regression_data staticmethod 
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generate_regression_data(
    n_samples: int = 1000,
    n_features: int = 10,
    noise_level: float = 0.1,
    random_state: int = 42,
    include_interactions: bool = True,
    include_nonlinear: bool = True,
) -> tuple

Generate synthetic regression data.

Parameters:

Name Type Description Default
n_samples int

Number of samples

 1000
n_features int

Number of features

 10
noise_level float

Level of noise to add

 0.1
random_state int

Random seed

 42
include_interactions bool

Whether to include feature interactions

 True
include_nonlinear bool

Whether to include nonlinear relationships

 True

Returns:

Type Description
tuple

Tuple of (X_train, X_test, y_train, y_test)
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_regression_data(
    n_samples: int = 1000,
    n_features: int = 10,
    noise_level: float = 0.1,
    random_state: int = 42,
    include_interactions: bool = True,
    include_nonlinear: bool = True,
) -> tuple:
    """Generate synthetic regression data.

    Args:
        n_samples: Number of samples
        n_features: Number of features
        noise_level: Level of noise to add
        random_state: Random seed
        include_interactions: Whether to include feature interactions
        include_nonlinear: Whether to include nonlinear relationships

    Returns:
        Tuple of (X_train, X_test, y_train, y_test)
    """
    np.random.seed(random_state)

    # Generate features
    X = np.random.normal(0, 1, (n_samples, n_features))

    # Add nonlinear relationships
    if include_nonlinear:
        X[:, 0] = X[:, 0] ** 2  # Quadratic relationship
        X[:, 1] = np.sin(X[:, 1])  # Sinusoidal relationship
        if n_features > 2:
            X[:, 2] = np.exp(X[:, 2] * 0.5)  # Exponential relationship

    # Add interactions
    if include_interactions and n_features >= 4:
        X[:, 3] = X[:, 2] * X[:, 3]  # Interaction term

    # Generate target with noise
    true_weights = np.random.normal(0, 1, n_features)
    y = np.dot(X, true_weights) + noise_level * np.random.normal(0, 1, n_samples)

    # Normalize features
    X_mean = np.mean(X, axis=0)
    X_std = np.std(X, axis=0)
    X_normalized = (X - X_mean) / (X_std + 1e-8)

    # Split data
    train_size = int(0.8 * n_samples)
    X_train = X_normalized[:train_size]
    X_test = X_normalized[train_size:]
    y_train = y[:train_size]
    y_test = y[train_size:]

    return X_train, X_test, y_train, y_test
generate_classification_data staticmethod 
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generate_classification_data(
    n_samples: int = 1000,
    n_features: int = 10,
    n_classes: int = 2,
    noise_level: float = 0.1,
    include_interactions: bool = True,
    include_nonlinear: bool = True,
    random_state: int = 42,
    sparse_features: bool = True,
    sparse_ratio: float = 0.3,
) -> tuple

Generate synthetic classification data.

Parameters:

Name Type Description Default
n_samples int

Number of samples

 1000
n_features int

Number of features

 10
n_classes int

Number of classes

 2
noise_level float

Level of noise to add

 0.1
include_interactions bool

Whether to include feature interactions

 True
include_nonlinear bool

Whether to include nonlinear relationships

 True
random_state int

Random seed

 42
sparse_features bool

Whether to create sparse features

 True
sparse_ratio float

Ratio of features that are relevant

 0.3

Returns:

Type Description
tuple

Tuple of (X_train, X_test, y_train, y_test)
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_classification_data(
    n_samples: int = 1000,
    n_features: int = 10,
    n_classes: int = 2,
    noise_level: float = 0.1,
    include_interactions: bool = True,
    include_nonlinear: bool = True,
    random_state: int = 42,
    sparse_features: bool = True,
    sparse_ratio: float = 0.3,
) -> tuple:
    """Generate synthetic classification data.

    Args:
        n_samples: Number of samples
        n_features: Number of features
        n_classes: Number of classes
        noise_level: Level of noise to add
        include_interactions: Whether to include feature interactions
        include_nonlinear: Whether to include nonlinear relationships
        random_state: Random seed
        sparse_features: Whether to create sparse features
        sparse_ratio: Ratio of features that are relevant

    Returns:
        Tuple of (X_train, X_test, y_train, y_test)
    """
    np.random.seed(random_state)

    # Generate features
    X = np.random.normal(0, 1, (n_samples, n_features))

    # Add nonlinear relationships
    if include_nonlinear:
        X[:, 0] = X[:, 0] ** 2  # Quadratic relationship
        X[:, 1] = np.sin(X[:, 1])  # Sinusoidal relationship
        if n_features > 2:
            X[:, 2] = np.exp(X[:, 2] * 0.5)  # Exponential relationship

    # Add interactions
    if include_interactions and n_features >= 4:
        X[:, 3] = X[:, 2] * X[:, 3]  # Interaction term

    # Create sparse features if requested
    if sparse_features:
        sparse_mask = np.random.random(n_features) < sparse_ratio
        X_sparse = X.copy()
        X_sparse[:, ~sparse_mask] = 0
        X = X_sparse
    else:
        sparse_mask = np.ones(n_features, dtype=bool)  # All features are relevant

    # Create decision boundary
    if n_classes == 2:
        # Binary classification
        relevant_features = X[:, sparse_mask] if sparse_features else X
        decision_boundary = np.sum(relevant_features, axis=1) + 0.5 * np.sum(
            relevant_features**2,
            axis=1,
        )
        decision_boundary += noise_level * np.random.normal(0, 1, n_samples)
        y = (decision_boundary > np.median(decision_boundary)).astype(int)
    else:
        # Multi-class classification
        centers = np.random.normal(0, 2, (n_classes, n_features))
        y = np.zeros(n_samples)
        for i in range(n_samples):
            distances = [np.linalg.norm(X[i] - center) for center in centers]
            y[i] = np.argmin(distances)

    # Normalize features
    X_mean = np.mean(X, axis=0)
    X_std = np.std(X, axis=0)
    X_normalized = (X - X_mean) / (X_std + 1e-8)

    # Split data
    train_size = int(0.8 * n_samples)
    X_train = X_normalized[:train_size]
    X_test = X_normalized[train_size:]
    y_train = y[:train_size]
    y_test = y[train_size:]

    return X_train, X_test, y_train, y_test
generate_anomaly_detection_data staticmethod 
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generate_anomaly_detection_data(
    n_normal: int = 1000,
    n_anomalies: int = 50,
    n_features: int = 50,
    random_state: int = 42,
    anomaly_type: str = "outlier",
) -> tuple

Generate synthetic anomaly detection data.

Parameters:

Name Type Description Default
n_normal int

Number of normal samples

 1000
n_anomalies int

Number of anomaly samples

 50
n_features int

Number of features

 50
random_state int

Random seed

 42
anomaly_type str

Type of anomalies ("outlier", "cluster", "drift")

 'outlier'

Returns:

Type Description
tuple

Tuple of (X_train, X_test, y_train, y_test)
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_anomaly_detection_data(
    n_normal: int = 1000,
    n_anomalies: int = 50,
    n_features: int = 50,
    random_state: int = 42,
    anomaly_type: str = "outlier",
) -> tuple:
    """Generate synthetic anomaly detection data.

    Args:
        n_normal: Number of normal samples
        n_anomalies: Number of anomaly samples
        n_features: Number of features
        random_state: Random seed
        anomaly_type: Type of anomalies ("outlier", "cluster", "drift")

    Returns:
        Tuple of (X_train, X_test, y_train, y_test)
    """
    np.random.seed(random_state)

    # Generate normal data (clustered)
    centers = [np.random.normal(0, 2, n_features) for _ in range(3)]
    normal_data = []
    for center in centers:
        cluster_data = np.random.normal(center, 1.0, (n_normal // 3, n_features))
        normal_data.append(cluster_data)

    # Add remaining samples to the last center
    remaining = n_normal - len(normal_data) * (n_normal // 3)
    if remaining > 0:
        last_center = centers[-1]
        remaining_data = np.random.normal(last_center, 1.0, (remaining, n_features))
        normal_data.append(remaining_data)

    normal_data_array = (
        np.vstack(normal_data)
        if normal_data
        else np.array([]).reshape(0, n_features)
    )

    # Generate anomaly data
    if anomaly_type == "outlier":
        anomaly_data = np.random.uniform(-10, 10, (n_anomalies, n_features))
    elif anomaly_type == "cluster":
        anomaly_center = np.random.normal(0, 5, n_features)
        anomaly_data = np.random.normal(
            anomaly_center,
            0.5,
            (n_anomalies, n_features),
        )
    elif anomaly_type == "drift":
        # Drift: same distribution but shifted
        drift_center = np.random.normal(3, 1, n_features)
        anomaly_data = np.random.normal(
            drift_center,
            1.0,
            (n_anomalies, n_features),
        )
    else:
        raise ValueError(f"Unknown anomaly type: {anomaly_type}")

    # Combine data
    all_data = np.vstack([normal_data_array, anomaly_data])
    labels = np.hstack([np.zeros(n_normal), np.ones(n_anomalies)])

    # Normalize data
    mean = np.mean(all_data, axis=0)
    std = np.std(all_data, axis=0)
    scaled_data = (all_data - mean) / (std + 1e-8)

    # Split data
    train_size = int(0.8 * len(scaled_data))
    X_train = scaled_data[:train_size]
    X_test = scaled_data[train_size:]
    y_train = labels[:train_size]
    y_test = labels[train_size:]

    return X_train, X_test, y_train, y_test
generate_context_data staticmethod 
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generate_context_data(
    n_samples: int = 1500,
    n_features: int = 15,
    n_context: int = 8,
    random_state: int = 42,
    context_effect: float = 0.3,
) -> tuple

Generate synthetic data with context information.

Parameters:

Name Type Description Default
n_samples int

Number of samples

 1500
n_features int

Number of main features

 15
n_context int

Number of context features

 8
random_state int

Random seed

 42
context_effect float

Strength of context effect

 0.3

Returns:

Type Description
tuple

Tuple containing (X_train, X_test, context_train, context_test, y_train, y_test)
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_context_data(
    n_samples: int = 1500,
    n_features: int = 15,
    n_context: int = 8,
    random_state: int = 42,
    context_effect: float = 0.3,
) -> tuple:
    """Generate synthetic data with context information.

    Args:
        n_samples: Number of samples
        n_features: Number of main features
        n_context: Number of context features
        random_state: Random seed
        context_effect: Strength of context effect

    Returns:
        Tuple containing (X_train, X_test, context_train, context_test, y_train, y_test)
    """
    np.random.seed(random_state)

    # Generate main features
    X = np.random.normal(0, 1, (n_samples, n_features))

    # Generate context features (different distribution)
    context = np.random.uniform(-2, 2, (n_samples, n_context))

    # Create complex target that depends on both features and context
    context_weights = np.random.normal(0, 1, n_context)
    feature_weights = np.random.normal(0, 1, n_features)

    # Create context-dependent decision boundary
    context_effect_val = np.dot(context, context_weights)
    feature_effect = np.dot(X, feature_weights)
    interaction_effect = context_effect * np.sum(X[:, :5] * context[:, :5], axis=1)

    # Combine effects
    decision_boundary = feature_effect + context_effect_val + interaction_effect
    y = (decision_boundary > np.median(decision_boundary)).astype(int)

    # Normalize features
    X_mean = np.mean(X, axis=0)
    X_std = np.std(X, axis=0)
    X_normalized = (X - X_mean) / (X_std + 1e-8)

    context_mean = np.mean(context, axis=0)
    context_std = np.std(context, axis=0)
    context_normalized = (context - context_mean) / (context_std + 1e-8)

    # Split data
    train_size = int(0.8 * n_samples)
    X_train = X_normalized[:train_size]
    X_test = X_normalized[train_size:]
    context_train = context_normalized[:train_size]
    context_test = context_normalized[train_size:]
    y_train = y[:train_size]
    y_test = y[train_size:]

    return X_train, X_test, context_train, context_test, y_train, y_test
generate_multi_input_data staticmethod 
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generate_multi_input_data(
    n_samples: int = 1000,
    feature_shapes: dict[str, tuple[int, ...]] = None,
    random_state: int = 42,
    task_type: str = "regression",
) -> tuple

Generate multi-input data for preprocessing model testing.

Parameters:

Name Type Description Default
n_samples int

Number of samples

 1000
feature_shapes dict[str, tuple[int, ...]]

Dictionary mapping feature names to shapes

 None
random_state int

Random seed

 42
task_type str

Type of task - "regression" or "classification"

 'regression'

Returns:

Type Description
tuple

Tuple containing (X_train_dict, X_test_dict, y_train, y_test)
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_multi_input_data(
    n_samples: int = 1000,
    feature_shapes: dict[str, tuple[int, ...]] = None,
    random_state: int = 42,
    task_type: str = "regression",
) -> tuple:
    """Generate multi-input data for preprocessing model testing.

    Args:
        n_samples: Number of samples
        feature_shapes: Dictionary mapping feature names to shapes
        random_state: Random seed
        task_type: Type of task - "regression" or "classification"

    Returns:
        Tuple containing (X_train_dict, X_test_dict, y_train, y_test)
    """
    if feature_shapes is None:
        feature_shapes = {"feature1": (20,), "feature2": (15,), "feature3": (10,)}

    np.random.seed(random_state)

    X_train_dict = {}
    X_test_dict = {}

    # Generate data for each feature
    for feature_name, shape in feature_shapes.items():
        # Generate random data with different distributions for each feature
        if "feature1" in feature_name:
            data = np.random.normal(0, 1, (n_samples,) + shape)
        elif "feature2" in feature_name:
            data = np.random.uniform(-2, 2, (n_samples,) + shape)
        else:
            data = np.random.exponential(1, (n_samples,) + shape)

        # Normalize
        data_mean = np.mean(data, axis=0)
        data_std = np.std(data, axis=0)
        data_normalized = (data - data_mean) / (data_std + 1e-8)

        # Split
        train_size = int(0.8 * n_samples)
        X_train_dict[feature_name] = data_normalized[:train_size]
        X_test_dict[feature_name] = data_normalized[train_size:]

    # Generate target based on combined features (use full dataset before splitting)
    combined_features = np.concatenate(
        [
            np.vstack([X_train_dict[name], X_test_dict[name]])
            for name in feature_shapes.keys()
        ],
        axis=1,
    )
    target_weights = np.random.normal(0, 1, combined_features.shape[1])
    y = np.dot(combined_features, target_weights) + 0.1 * np.random.normal(
        0,
        1,
        combined_features.shape[0],
    )

    # Convert to classification if requested
    if task_type == "classification":
        y = (y > np.median(y)).astype(int)

    # Split target
    train_size = int(0.8 * n_samples)
    y_train = y[:train_size]
    y_test = y[train_size:]

    return X_train_dict, X_test_dict, y_train, y_test
create_preprocessing_model staticmethod 
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create_preprocessing_model(
    input_shapes: dict[str, tuple[int, ...]],
    output_dim: int = 32,
    name: str = "preprocessing_model",
) -> keras.Model

Create a preprocessing model for multi-input data.

Parameters:

Name Type Description Default
input_shapes dict[str, tuple[int, ...]]

Dictionary mapping input names to shapes

 required
output_dim int

Output dimension

 32
name str

Model name

 'preprocessing_model'

Returns:

Type Description
Model

Keras preprocessing model
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def create_preprocessing_model(
    input_shapes: dict[str, tuple[int, ...]],
    output_dim: int = 32,
    name: str = "preprocessing_model",
) -> keras.Model:
    """Create a preprocessing model for multi-input data.

    Args:
        input_shapes: Dictionary mapping input names to shapes
        output_dim: Output dimension
        name: Model name

    Returns:
        Keras preprocessing model
    """
    # Create input layers
    inputs = {}
    processed_inputs = []

    for input_name, input_shape in input_shapes.items():
        inputs[input_name] = keras.layers.Input(shape=input_shape, name=input_name)

        # Process each input
        if len(input_shape) == 1:
            # 1D input - use dense layers
            x = keras.layers.Dense(16, activation="relu")(inputs[input_name])
            x = keras.layers.Dropout(0.1)(x)
            x = keras.layers.Dense(16, activation="relu")(x)
        else:
            # Multi-dimensional input - use flatten + dense
            x = keras.layers.Flatten()(inputs[input_name])
            x = keras.layers.Dense(32, activation="relu")(x)
            x = keras.layers.Dropout(0.1)(x)
            x = keras.layers.Dense(16, activation="relu")(x)

        processed_inputs.append(x)

    # Combine processed inputs
    if len(processed_inputs) > 1:
        combined = keras.layers.Concatenate()(processed_inputs)
    else:
        combined = processed_inputs[0]

    # Final processing
    output = keras.layers.Dense(output_dim, activation="relu")(combined)
    output = keras.layers.Dropout(0.1)(output)

    # Create model
    model = keras.Model(inputs=inputs, outputs=output, name=name)

    return model
create_dataset staticmethod 
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create_dataset(
    X: Union[np.ndarray, dict[str, np.ndarray]],
    y: np.ndarray,
    batch_size: int = 32,
    shuffle: bool = True,
) -> tf.data.Dataset

Create a TensorFlow dataset from data (requires TensorFlow).

Parameters:

Name Type Description Default
X Union[ndarray, dict[str, ndarray]]

Input data (array or dict of arrays)

 required
y ndarray

Target data

 required
batch_size int

Batch size

 32
shuffle bool

Whether to shuffle data

 True

Returns:

Type Description
Dataset

TensorFlow dataset (if TensorFlow is available)

Raises:

Type Description
ImportError

If TensorFlow is not installed
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def create_dataset(
    X: Union[np.ndarray, dict[str, np.ndarray]],
    y: np.ndarray,
    batch_size: int = 32,
    shuffle: bool = True,
) -> "tf.data.Dataset":
    """Create a TensorFlow dataset from data (requires TensorFlow).

    Args:
        X: Input data (array or dict of arrays)
        y: Target data
        batch_size: Batch size
        shuffle: Whether to shuffle data

    Returns:
        TensorFlow dataset (if TensorFlow is available)

    Raises:
        ImportError: If TensorFlow is not installed
    """
    if not _TENSORFLOW_AVAILABLE:
        raise ImportError(
            "TensorFlow is required for create_dataset. "
            "Install it with: pip install tensorflow",
        )

    if isinstance(X, dict):
        # Multi-input data
        dataset = tf.data.Dataset.from_tensor_slices((X, y))
    else:
        # Single input data
        dataset = tf.data.Dataset.from_tensor_slices((X, y))

    if shuffle:
        dataset = dataset.shuffle(buffer_size=len(y))

    dataset = dataset.batch(batch_size)

    return dataset
generate_timeseries_data staticmethod 
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generate_timeseries_data(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    random_state: int = 42,
    include_trend: bool = True,
    include_seasonality: bool = True,
    trend_direction: str = "up",
    noise_level: float = 0.1,
    scale: float = 1.0,
) -> tuple

Generate synthetic multivariate time series data for forecasting.

This method generates realistic time series data with optional trend and seasonality patterns, suitable for testing time series models like TSMixer and TimeMixer.

Parameters:

Name Type Description Default
n_samples int

Number of time series samples to generate.

 1000
seq_len int

Length of input sequence (lookback window).

 96
pred_len int

Length of prediction horizon (forecast window).

 12
n_features int

Number of time series features (channels).

 7
random_state int

Random seed for reproducibility.

 42
include_trend bool

Whether to include trend component.

 True
include_seasonality bool

Whether to include seasonal component.

 True
trend_direction str

Direction of trend ('up', 'down', 'random').

 'up'
noise_level float

Standard deviation of Gaussian noise.

 0.1
scale float

Scaling factor for the generated data.

 1.0

Returns:

Type Description
tuple

Tuple of (X, y) where:
tuple
  • X: Input sequences of shape (n_samples, seq_len, n_features)
tuple
  • y: Target sequences of shape (n_samples, pred_len, n_features)
Example

X, y = KerasFactoryDataGenerator.generate_timeseries_data( ... n_samples=100, ... seq_len=96, ... pred_len=12, ... n_features=7 ... ) X.shape (100, 96, 7) y.shape (100, 12, 7)

Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_timeseries_data(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    random_state: int = 42,
    include_trend: bool = True,
    include_seasonality: bool = True,
    trend_direction: str = "up",
    noise_level: float = 0.1,
    scale: float = 1.0,
) -> tuple:
    """Generate synthetic multivariate time series data for forecasting.

    This method generates realistic time series data with optional trend and
    seasonality patterns, suitable for testing time series models like TSMixer
    and TimeMixer.

    Args:
        n_samples: Number of time series samples to generate.
        seq_len: Length of input sequence (lookback window).
        pred_len: Length of prediction horizon (forecast window).
        n_features: Number of time series features (channels).
        random_state: Random seed for reproducibility.
        include_trend: Whether to include trend component.
        include_seasonality: Whether to include seasonal component.
        trend_direction: Direction of trend ('up', 'down', 'random').
        noise_level: Standard deviation of Gaussian noise.
        scale: Scaling factor for the generated data.

    Returns:
        Tuple of (X, y) where:
        - X: Input sequences of shape (n_samples, seq_len, n_features)
        - y: Target sequences of shape (n_samples, pred_len, n_features)

    Example:
        >>> X, y = KerasFactoryDataGenerator.generate_timeseries_data(
        ...     n_samples=100,
        ...     seq_len=96,
        ...     pred_len=12,
        ...     n_features=7
        ... )
        >>> X.shape
        (100, 96, 7)
        >>> y.shape
        (100, 12, 7)
    """
    np.random.seed(random_state)

    total_len = seq_len + pred_len
    X = np.zeros((n_samples, seq_len, n_features), dtype=np.float32)
    y = np.zeros((n_samples, pred_len, n_features), dtype=np.float32)

    for sample_idx in range(n_samples):
        for feature_idx in range(n_features):
            # Generate time steps
            t = np.arange(total_len)

            # Initialize time series
            ts = np.zeros(total_len)

            # Add trend
            if include_trend:
                if trend_direction == "up":
                    trend = np.linspace(0, 1, total_len) * scale
                elif trend_direction == "down":
                    trend = np.linspace(1, 0, total_len) * scale
                else:  # random
                    trend_slope = np.random.uniform(-0.5, 0.5)
                    trend = trend_slope * t / total_len * scale
                ts += trend

            # Add seasonality
            if include_seasonality:
                seasonal_period = np.random.randint(7, 25)
                seasonal_amplitude = np.random.uniform(0.2, 0.8) * scale
                seasonality = seasonal_amplitude * np.sin(
                    2 * np.pi * t / seasonal_period,
                )
                ts += seasonality

            # Add base level
            base_level = np.random.uniform(-1, 1) * scale
            ts += base_level

            # Add noise
            noise = np.random.normal(0, noise_level, total_len)
            ts += noise

            # Split into input and target
            X[sample_idx, :, feature_idx] = ts[:seq_len]
            y[sample_idx, :, feature_idx] = ts[seq_len:]

    return X.astype(np.float32), y.astype(np.float32)
generate_multivariate_timeseries staticmethod 
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generate_multivariate_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    correlation_strength: float = 0.5,
    random_state: int = 42,
) -> tuple

Generate correlated multivariate time series data.

Creates time series where features have dependencies on each other, simulating real-world scenarios where different variables influence one another.

Parameters:

Name Type Description Default
n_samples int

Number of samples.

 1000
seq_len int

Input sequence length.

 96
pred_len int

Prediction horizon.

 12
n_features int

Number of features.

 7
correlation_strength float

Strength of inter-feature correlations (0-1).

 0.5
random_state int

Random seed.

 42

Returns:

Type Description
tuple

Tuple of (X, y) where X has shape (n_samples, seq_len, n_features)
tuple

and y has shape (n_samples, pred_len, n_features).
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_multivariate_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    correlation_strength: float = 0.5,
    random_state: int = 42,
) -> tuple:
    """Generate correlated multivariate time series data.

    Creates time series where features have dependencies on each other,
    simulating real-world scenarios where different variables influence
    one another.

    Args:
        n_samples: Number of samples.
        seq_len: Input sequence length.
        pred_len: Prediction horizon.
        n_features: Number of features.
        correlation_strength: Strength of inter-feature correlations (0-1).
        random_state: Random seed.

    Returns:
        Tuple of (X, y) where X has shape (n_samples, seq_len, n_features)
        and y has shape (n_samples, pred_len, n_features).
    """
    np.random.seed(random_state)

    total_len = seq_len + pred_len
    X = np.zeros((n_samples, seq_len, n_features), dtype=np.float32)
    y = np.zeros((n_samples, pred_len, n_features), dtype=np.float32)

    # Generate correlation matrix
    if n_features > 1:
        # Create positive semi-definite correlation matrix
        L = np.random.randn(n_features, n_features)
        corr_matrix = correlation_strength * (L @ L.T)
        corr_matrix /= np.diag(corr_matrix).max()
        np.fill_diagonal(corr_matrix, 1.0)
    else:
        corr_matrix = np.array([[1.0]])

    for sample_idx in range(n_samples):
        # Generate independent noise
        noise = np.random.randn(total_len, n_features)

        # Apply correlation
        ts_data = (
            noise @ np.linalg.cholesky(corr_matrix + np.eye(n_features) * 0.01).T
        )

        # Add trend to first feature
        trend = np.linspace(0, 1, total_len)
        ts_data[:, 0] += trend

        # Split
        X[sample_idx, :, :] = ts_data[:seq_len]
        y[sample_idx, :, :] = ts_data[seq_len:]

    return X.astype(np.float32), y.astype(np.float32)
generate_seasonal_timeseries staticmethod 
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generate_seasonal_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    seasonal_period: int = 12,
    random_state: int = 42,
) -> tuple

Generate strongly seasonal time series data.

Ideal for testing decomposition-based models like TimeMixer that explicitly handle trend and seasonal components.

Parameters:

Name Type Description Default
n_samples int

Number of samples.

 1000
seq_len int

Input sequence length.

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pred_len int

Prediction horizon.

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n_features int

Number of features.

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seasonal_period int

Period of seasonality.

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random_state int

Random seed.

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Returns:

Type Description
tuple

Tuple of (X, y).
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_seasonal_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    seasonal_period: int = 12,
    random_state: int = 42,
) -> tuple:
    """Generate strongly seasonal time series data.

    Ideal for testing decomposition-based models like TimeMixer that
    explicitly handle trend and seasonal components.

    Args:
        n_samples: Number of samples.
        seq_len: Input sequence length.
        pred_len: Prediction horizon.
        n_features: Number of features.
        seasonal_period: Period of seasonality.
        random_state: Random seed.

    Returns:
        Tuple of (X, y).
    """
    np.random.seed(random_state)

    total_len = seq_len + pred_len
    X = np.zeros((n_samples, seq_len, n_features), dtype=np.float32)
    y = np.zeros((n_samples, pred_len, n_features), dtype=np.float32)

    for sample_idx in range(n_samples):
        for feature_idx in range(n_features):
            t = np.arange(total_len)

            # Base trend (slowly changing)
            base_trend = 50 + 20 * np.sin(2 * np.pi * t / (total_len * 2))

            # Strong seasonality
            seasonal = 10 * np.sin(2 * np.pi * t / seasonal_period)

            # Feature-specific cycle
            cycle = 5 * np.cos(2 * np.pi * (t / 30 + feature_idx / n_features))

            # Combine components
            ts = base_trend + seasonal + cycle

            # Add small noise
            ts += np.random.normal(0, 0.5, total_len)

            # Split
            X[sample_idx, :, feature_idx] = ts[:seq_len]
            y[sample_idx, :, feature_idx] = ts[seq_len:]

    return X.astype(np.float32), y.astype(np.float32)
generate_anomalous_timeseries staticmethod 
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generate_anomalous_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    anomaly_ratio: float = 0.1,
    anomaly_magnitude: float = 3.0,
    random_state: int = 42,
) -> tuple

Generate time series with anomalies for anomaly detection testing.

Parameters:

Name Type Description Default
n_samples int

Number of samples.

 1000
seq_len int

Input sequence length.

 96
pred_len int

Prediction horizon.

 12
n_features int

Number of features.

 7
anomaly_ratio float

Ratio of anomalous samples (0-1).

 0.1
anomaly_magnitude float

Magnitude of anomalies in std deviations.

 3.0
random_state int

Random seed.

 42

Returns:

Type Description
tuple

Tuple of (X, y, anomaly_labels) where anomaly_labels indicates
tuple

which samples contain anomalies.
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_anomalous_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    anomaly_ratio: float = 0.1,
    anomaly_magnitude: float = 3.0,
    random_state: int = 42,
) -> tuple:
    """Generate time series with anomalies for anomaly detection testing.

    Args:
        n_samples: Number of samples.
        seq_len: Input sequence length.
        pred_len: Prediction horizon.
        n_features: Number of features.
        anomaly_ratio: Ratio of anomalous samples (0-1).
        anomaly_magnitude: Magnitude of anomalies in std deviations.
        random_state: Random seed.

    Returns:
        Tuple of (X, y, anomaly_labels) where anomaly_labels indicates
        which samples contain anomalies.
    """
    np.random.seed(random_state)

    # First generate normal data
    X, y = KerasFactoryDataGenerator.generate_timeseries_data(
        n_samples=n_samples,
        seq_len=seq_len,
        pred_len=pred_len,
        n_features=n_features,
        random_state=random_state,
    )

    # Create anomaly labels
    anomaly_labels = np.zeros(n_samples, dtype=np.int32)
    n_anomalies = int(n_samples * anomaly_ratio)
    anomaly_indices = np.random.choice(n_samples, n_anomalies, replace=False)
    anomaly_labels[anomaly_indices] = 1

    # Inject anomalies
    for idx in anomaly_indices:
        # Choose random anomaly type
        anomaly_type = np.random.choice(["spike", "drift", "noise"])

        for feature_idx in range(n_features):
            if anomaly_type == "spike":
                # Sudden spike
                spike_pos = np.random.randint(0, seq_len)
                X[idx, spike_pos, feature_idx] += anomaly_magnitude * np.std(
                    X[:, spike_pos, feature_idx],
                )

            elif anomaly_type == "drift":
                # Gradual drift
                drift = np.linspace(0, anomaly_magnitude, seq_len) * np.std(
                    X[:, :, feature_idx],
                )
                X[idx, :, feature_idx] += drift

            else:  # noise
                # Excessive noise
                noise = np.random.normal(0, anomaly_magnitude, seq_len) * np.std(
                    X[:, :, feature_idx],
                )
                X[idx, :, feature_idx] += noise

    return (
        X.astype(np.float32),
        y.astype(np.float32),
        anomaly_labels.astype(np.int32),
    )
generate_multiscale_timeseries staticmethod 
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generate_multiscale_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    scales: list[int] | None = None,
    random_state: int = 42,
) -> tuple

Generate multi-scale time series with components at different frequencies.

Useful for testing models that use multi-scale mixing like TimeMixer.

Parameters:

Name Type Description Default
n_samples int

Number of samples.

 1000
seq_len int

Input sequence length.

 96
pred_len int

Prediction horizon.

 12
n_features int

Number of features.

 7
scales list[int] | None

List of frequency scales (default: [7, 14, 28, 56]).

 None
random_state int

Random seed.

 42

Returns:

Type Description
tuple

Tuple of (X, y).
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_multiscale_timeseries(
    n_samples: int = 1000,
    seq_len: int = 96,
    pred_len: int = 12,
    n_features: int = 7,
    scales: list[int] | None = None,
    random_state: int = 42,
) -> tuple:
    """Generate multi-scale time series with components at different frequencies.

    Useful for testing models that use multi-scale mixing like TimeMixer.

    Args:
        n_samples: Number of samples.
        seq_len: Input sequence length.
        pred_len: Prediction horizon.
        n_features: Number of features.
        scales: List of frequency scales (default: [7, 14, 28, 56]).
        random_state: Random seed.

    Returns:
        Tuple of (X, y).
    """
    if scales is None:
        scales = [7, 14, 28, 56]

    np.random.seed(random_state)

    total_len = seq_len + pred_len
    X = np.zeros((n_samples, seq_len, n_features), dtype=np.float32)
    y = np.zeros((n_samples, pred_len, n_features), dtype=np.float32)

    for sample_idx in range(n_samples):
        for feature_idx in range(n_features):
            t = np.arange(total_len)
            ts = np.zeros(total_len)

            # Add components at different scales
            for scale_idx, scale in enumerate(scales):
                amplitude = 1.0 / (scale_idx + 1)  # Decreasing amplitude
                ts += amplitude * np.sin(2 * np.pi * t / scale)

            # Add trend
            ts += 0.1 * t / total_len

            # Add noise
            ts += np.random.normal(0, 0.1, total_len)

            X[sample_idx, :, feature_idx] = ts[:seq_len]
            y[sample_idx, :, feature_idx] = ts[seq_len:]

    return X.astype(np.float32), y.astype(np.float32)
generate_long_horizon_timeseries staticmethod 
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generate_long_horizon_timeseries(
    n_samples: int = 500,
    seq_len: int = 336,
    pred_len: int = 336,
    n_features: int = 7,
    random_state: int = 42,
) -> tuple

Generate long-horizon time series for testing long-term forecasting.

Useful for benchmarking models on challenging long-range forecasting tasks.

Parameters:

Name Type Description Default
n_samples int

Number of samples.

 500
seq_len int

Input sequence length (typically 336 = 2 weeks of hourly data).

 336
pred_len int

Prediction horizon (typically 336 for long-horizon).

 336
n_features int

Number of features.

 7
random_state int

Random seed.

 42

Returns:

Type Description
tuple

Tuple of (X, y).
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_long_horizon_timeseries(
    n_samples: int = 500,
    seq_len: int = 336,
    pred_len: int = 336,
    n_features: int = 7,
    random_state: int = 42,
) -> tuple:
    """Generate long-horizon time series for testing long-term forecasting.

    Useful for benchmarking models on challenging long-range forecasting tasks.

    Args:
        n_samples: Number of samples.
        seq_len: Input sequence length (typically 336 = 2 weeks of hourly data).
        pred_len: Prediction horizon (typically 336 for long-horizon).
        n_features: Number of features.
        random_state: Random seed.

    Returns:
        Tuple of (X, y).
    """
    np.random.seed(random_state)

    total_len = seq_len + pred_len
    X = np.zeros((n_samples, seq_len, n_features), dtype=np.float32)
    y = np.zeros((n_samples, pred_len, n_features), dtype=np.float32)

    for sample_idx in range(n_samples):
        for feature_idx in range(n_features):
            t = np.arange(total_len)

            # Weekly seasonality
            weekly = 10 * np.sin(2 * np.pi * t / 168)

            # Daily seasonality
            daily = 5 * np.sin(2 * np.pi * t / 24)

            # Long-term trend (very slow)
            long_trend = 2 * np.sin(2 * np.pi * t / (total_len * 4))

            # Combine
            ts = 50 + weekly + daily + long_trend

            # Add noise
            ts += np.random.normal(0, 0.3, total_len)

            X[sample_idx, :, feature_idx] = ts[:seq_len]
            y[sample_idx, :, feature_idx] = ts[seq_len:]

    return X.astype(np.float32), y.astype(np.float32)
generate_synthetic_energy_demand staticmethod 
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generate_synthetic_energy_demand(
    n_samples: int = 1000,
    seq_len: int = 168,
    pred_len: int = 24,
    n_features: int = 3,
    random_state: int = 42,
) -> tuple

Generate synthetic energy demand time series.

Simulates realistic energy consumption patterns with daily and weekly seasonality, useful for testing on realistic forecasting scenarios.

Parameters:

Name Type Description Default
n_samples int

Number of samples.

 1000
seq_len int

Input sequence length (default: 168 = 1 week).

 168
pred_len int

Prediction horizon (default: 24 = 1 day).

 24
n_features int

Number of features (e.g., residential, commercial, industrial).

 3
random_state int

Random seed.

 42

Returns:

Type Description
tuple

Tuple of (X, y).
Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def generate_synthetic_energy_demand(
    n_samples: int = 1000,
    seq_len: int = 168,
    pred_len: int = 24,
    n_features: int = 3,
    random_state: int = 42,
) -> tuple:
    """Generate synthetic energy demand time series.

    Simulates realistic energy consumption patterns with daily and weekly
    seasonality, useful for testing on realistic forecasting scenarios.

    Args:
        n_samples: Number of samples.
        seq_len: Input sequence length (default: 168 = 1 week).
        pred_len: Prediction horizon (default: 24 = 1 day).
        n_features: Number of features (e.g., residential, commercial, industrial).
        random_state: Random seed.

    Returns:
        Tuple of (X, y).
    """
    np.random.seed(random_state)

    total_len = seq_len + pred_len
    X = np.zeros((n_samples, seq_len, n_features), dtype=np.float32)
    y = np.zeros((n_samples, pred_len, n_features), dtype=np.float32)

    # Base demand for each sector
    base_demands = [100, 80, 50]  # Residential, Commercial, Industrial

    for sample_idx in range(n_samples):
        for feature_idx in range(min(n_features, len(base_demands))):
            t = np.arange(total_len)
            base = base_demands[feature_idx]

            # Daily pattern (peak during day, low at night)
            hour_of_day = t % 24
            daily_pattern = base * (
                1 + 0.3 * np.sin(np.pi * hour_of_day / 12 - np.pi / 2)
            )

            # Weekly pattern (higher on weekdays)
            day_of_week = (t // 24) % 7
            weekly_pattern = 1 + 0.1 * np.cos(np.pi * day_of_week / 7)

            # Temperature effect (simplified)
            temp_effect = 5 * np.sin(2 * np.pi * t / total_len)

            # Combine
            demand = daily_pattern * weekly_pattern + temp_effect

            # Add noise
            demand += np.random.normal(0, 2, total_len)

            X[sample_idx, :, feature_idx] = np.maximum(demand[:seq_len], 0)
            y[sample_idx, :, feature_idx] = np.maximum(demand[seq_len:], 0)

        # For additional features, repeat with variations
        for feature_idx in range(len(base_demands), n_features):
            X[sample_idx, :, feature_idx] = X[
                sample_idx,
                :,
                feature_idx % len(base_demands),
            ] * np.random.uniform(0.8, 1.2)
            y[sample_idx, :, feature_idx] = y[
                sample_idx,
                :,
                feature_idx % len(base_demands),
            ] * np.random.uniform(0.8, 1.2)

    return X.astype(np.float32), y.astype(np.float32)
create_timeseries_dataset staticmethod 
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create_timeseries_dataset(
    X: np.ndarray,
    y: np.ndarray,
    batch_size: int = 32,
    shuffle: bool = True,
) -> tf.data.Dataset

Create a TensorFlow dataset from time series data (requires TensorFlow).

Parameters:

Name Type Description Default
X ndarray

Input sequences of shape (n_samples, seq_len, n_features).

 required
y ndarray

Target sequences of shape (n_samples, pred_len, n_features).

 required
batch_size int

Batch size.

 32
shuffle bool

Whether to shuffle data.

 True

Returns:

Type Description
Dataset

TensorFlow dataset with (X, y) pairs.

Raises:

Type Description
ImportError

If TensorFlow is not installed
Example

X, y = KerasFactoryDataGenerator.generate_timeseries_data() dataset = KerasFactoryDataGenerator.create_timeseries_dataset(X, y) for x_batch, y_batch in dataset.take(1): ... print(x_batch.shape, y_batch.shape)

Source code in kerasfactory/utils/data_generator.py 
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@staticmethod
def create_timeseries_dataset(
    X: np.ndarray,
    y: np.ndarray,
    batch_size: int = 32,
    shuffle: bool = True,
) -> "tf.data.Dataset":
    """Create a TensorFlow dataset from time series data (requires TensorFlow).

    Args:
        X: Input sequences of shape (n_samples, seq_len, n_features).
        y: Target sequences of shape (n_samples, pred_len, n_features).
        batch_size: Batch size.
        shuffle: Whether to shuffle data.

    Returns:
        TensorFlow dataset with (X, y) pairs.

    Raises:
        ImportError: If TensorFlow is not installed

    Example:
        >>> X, y = KerasFactoryDataGenerator.generate_timeseries_data()
        >>> dataset = KerasFactoryDataGenerator.create_timeseries_dataset(X, y)
        >>> for x_batch, y_batch in dataset.take(1):
        ...     print(x_batch.shape, y_batch.shape)
    """
    if not _TENSORFLOW_AVAILABLE:
        raise ImportError(
            "TensorFlow is required for create_timeseries_dataset. "
            "Install it with: pip install tensorflow",
        )

    dataset = tf.data.Dataset.from_tensor_slices((X, y))

    if shuffle:
        dataset = dataset.shuffle(buffer_size=len(X))

    dataset = dataset.batch(batch_size)
    dataset = dataset.prefetch(tf.data.AUTOTUNE)

    return dataset

Time Series Methods

generate_timeseries_data()

Generates synthetic multivariate time series with optional trend and seasonality.

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from kerasfactory.utils import KerasFactoryDataGenerator

X, y = KerasFactoryDataGenerator.generate_timeseries_data(
    n_samples=1000,
    seq_len=96,           # Input sequence length
    pred_len=12,          # Prediction horizon
    n_features=7,         # Number of channels
    include_trend=True,
    include_seasonality=True,
    trend_direction="up"  # "up", "down", "random"
)
generate_multivariate_timeseries()

Generates time series with inter-feature correlations for realistic multivariate data.

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X, y = KerasFactoryDataGenerator.generate_multivariate_timeseries(
    n_samples=1000,
    seq_len=96,
    pred_len=12,
    n_features=7,
    correlation_strength=0.5  # 0-1
)
generate_seasonal_timeseries()

Emphasized seasonal patterns, ideal for decomposition models like TimeMixer.

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X, y = KerasFactoryDataGenerator.generate_seasonal_timeseries(
    n_samples=1000,
    seq_len=96,
    pred_len=12,
    n_features=7,
    seasonal_period=12
)
generate_multiscale_timeseries()

Components at different frequencies for testing multi-scale mixing models.

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X, y = KerasFactoryDataGenerator.generate_multiscale_timeseries(
    n_samples=1000,
    seq_len=96,
    pred_len=12,
    n_features=7,
    scales=[7, 14, 28, 56]
)
generate_anomalous_timeseries()

Time series with injected anomalies for testing anomaly detection models.

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X, y, labels = KerasFactoryDataGenerator.generate_anomalous_timeseries(
    n_samples=1000,
    seq_len=96,
    pred_len=12,
    n_features=7,
    anomaly_ratio=0.1,        # 10% anomalies
    anomaly_magnitude=3.0     # 3 std deviations
)
generate_long_horizon_timeseries()

For benchmarking long-term forecasting (e.g., 2 weeks ahead).

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X, y = KerasFactoryDataGenerator.generate_long_horizon_timeseries(
    n_samples=500,
    seq_len=336,   # 2 weeks hourly
    pred_len=336,  # Forecast 2 weeks
    n_features=7
)
generate_synthetic_energy_demand()

Realistic energy consumption patterns with daily/weekly seasonality.

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X, y = KerasFactoryDataGenerator.generate_synthetic_energy_demand(
    n_samples=1000,
    seq_len=168,   # 1 week
    pred_len=24,   # 1 day forecast
    n_features=3   # Residential, Commercial, Industrial
)
create_timeseries_dataset()

Converts numpy arrays to TensorFlow datasets with batching and auto-tuning.

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dataset = KerasFactoryDataGenerator.create_timeseries_dataset(
    X=X_train,
    y=y_train,
    batch_size=32,
    shuffle=True
)

model.fit(dataset, epochs=10)

🎨 Visualization

📈 KerasFactoryPlotter

Utility class for creating consistent and professional visualizations for KerasFactory models, metrics, and data analysis.

Features: - Time Series Visualization: Multiple visualization styles for forecasts - Training History: Training and validation metrics - Classification Metrics: ROC, precision-recall, confusion matrix - Anomaly Detection: Anomaly score distributions - Performance Metrics: Bar charts and comparison visualizations

kerasfactory.utils.plotting.KerasFactoryPlotter

Utility class for creating consistent visualizations across KerasFactory notebooks.

Functions

plot_training_history staticmethod 
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plot_training_history(
    history: Any,
    metrics: list[str] = None,
    title: str = "Training Progress",
    height: int = 400,
) -> go.Figure

Create training history plots.

Parameters:

Name Type Description Default
history Any

Keras training history object or dict with history data

 required
metrics list[str]

List of metrics to plot (default: ['loss', 'accuracy'])

 None
title str

Plot title

 'Training Progress'
height int

Plot height

 400

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_training_history(
    history: Any,
    metrics: list[str] = None,
    title: str = "Training Progress",
    height: int = 400,
) -> go.Figure:
    """Create training history plots.

    Args:
        history: Keras training history object or dict with history data
        metrics: List of metrics to plot (default: ['loss', 'accuracy'])
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    if metrics is None:
        metrics = ["loss", "accuracy"]

    # Handle both History objects and dicts
    if isinstance(history, dict):
        hist_dict = history
    else:
        hist_dict = history.history

    # Determine subplot layout
    n_metrics = len(metrics)
    if n_metrics <= 2:
        rows, cols = 1, n_metrics
    elif n_metrics <= 4:
        rows, cols = 2, 2
    else:
        rows, cols = 3, 2

    fig = make_subplots(
        rows=rows,
        cols=cols,
        subplot_titles=[
            f"Training and Validation {metric.title()}" for metric in metrics
        ],
    )

    colors = ["blue", "red", "green", "orange", "purple", "brown"]

    for i, metric in enumerate(metrics):
        if metric in hist_dict:
            row = (i // cols) + 1
            col = (i % cols) + 1

            # Training metric
            fig.add_trace(
                go.Scatter(
                    x=list(range(1, len(hist_dict[metric]) + 1)),
                    y=hist_dict[metric],
                    mode="lines",
                    name=f"Training {metric.title()}",
                    line=dict(color=colors[0]),
                ),
                row=row,
                col=col,
            )

            # Validation metric
            val_metric = f"val_{metric}"
            if val_metric in hist_dict:
                fig.add_trace(
                    go.Scatter(
                        x=list(range(1, len(hist_dict[val_metric]) + 1)),
                        y=hist_dict[val_metric],
                        mode="lines",
                        name=f"Validation {metric.title()}",
                        line=dict(color=colors[1]),
                    ),
                    row=row,
                    col=col,
                )

    fig.update_layout(title_text=title, height=height, showlegend=True)
    return fig
plot_confusion_matrix staticmethod 
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plot_confusion_matrix(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Confusion Matrix",
    height: int = 400,
) -> go.Figure

Create confusion matrix heatmap.

Parameters:

Name Type Description Default
y_true ndarray

True labels

 required
y_pred ndarray

Predicted labels

 required
title str

Plot title

 'Confusion Matrix'
height int

Plot height

 400

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_confusion_matrix(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Confusion Matrix",
    height: int = 400,
) -> go.Figure:
    """Create confusion matrix heatmap.

    Args:
        y_true: True labels
        y_pred: Predicted labels
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    from collections import Counter

    # Create confusion matrix
    cm = Counter(zip(y_true, y_pred, strict=False))
    n_classes = len(np.unique(y_true))

    if n_classes == 2:
        cm_matrix = np.array(
            [
                [cm.get((0, 0), 0), cm.get((0, 1), 0)],
                [cm.get((1, 0), 0), cm.get((1, 1), 0)],
            ],
        )
        x_labels = ["Predicted 0", "Predicted 1"]
        y_labels = ["Actual 0", "Actual 1"]
    else:
        # Multi-class confusion matrix
        cm_matrix = np.zeros((n_classes, n_classes))
        for (true_label, pred_label), count in cm.items():
            cm_matrix[true_label, pred_label] = count
        x_labels = [f"Predicted {i}" for i in range(n_classes)]
        y_labels = [f"Actual {i}" for i in range(n_classes)]

    fig = go.Figure()

    fig.add_trace(
        go.Heatmap(
            z=cm_matrix,
            x=x_labels,
            y=y_labels,
            text=cm_matrix.astype(int),
            texttemplate="%{text}",
            textfont={"size": 16},
            colorscale="Blues",
        ),
    )

    fig.update_layout(title=title, height=height)

    return fig
plot_predictions_vs_actual staticmethod 
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plot_predictions_vs_actual(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Predictions vs Actual Values",
    height: int = 500,
) -> go.Figure

Create predictions vs actual values scatter plot.

Parameters:

Name Type Description Default
y_true ndarray

True values

 required
y_pred ndarray

Predicted values

 required
title str

Plot title

 'Predictions vs Actual Values'
height int

Plot height

 500

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_predictions_vs_actual(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Predictions vs Actual Values",
    height: int = 500,
) -> go.Figure:
    """Create predictions vs actual values scatter plot.

    Args:
        y_true: True values
        y_pred: Predicted values
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    fig = go.Figure()

    fig.add_trace(
        go.Scatter(
            x=y_true,
            y=y_pred,
            mode="markers",
            name="Predictions",
            marker=dict(color="blue", opacity=0.6),
        ),
    )

    # Add perfect prediction line
    min_val = min(y_true.min(), y_pred.min())
    max_val = max(y_true.max(), y_pred.max())
    fig.add_trace(
        go.Scatter(
            x=[min_val, max_val],
            y=[min_val, max_val],
            mode="lines",
            name="Perfect Prediction",
            line=dict(color="red", dash="dash"),
        ),
    )

    fig.update_layout(
        title=title,
        xaxis_title="Actual Values",
        yaxis_title="Predicted Values",
        height=height,
    )

    return fig
plot_anomaly_scores staticmethod 
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plot_anomaly_scores(
    scores: np.ndarray,
    labels: np.ndarray,
    threshold: float = None,
    title: str = "Anomaly Score Distribution",
    height: int = 400,
) -> go.Figure

Create anomaly score distribution plot.

Parameters:

Name Type Description Default
scores ndarray

Anomaly scores

 required
labels ndarray

True labels (0=normal, 1=anomaly)

 required
threshold float

Anomaly threshold

 None
title str

Plot title

 'Anomaly Score Distribution'
height int

Plot height

 400

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_anomaly_scores(
    scores: np.ndarray,
    labels: np.ndarray,
    threshold: float = None,
    title: str = "Anomaly Score Distribution",
    height: int = 400,
) -> go.Figure:
    """Create anomaly score distribution plot.

    Args:
        scores: Anomaly scores
        labels: True labels (0=normal, 1=anomaly)
        threshold: Anomaly threshold
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    fig = go.Figure()

    # Separate scores by label
    normal_scores = scores[labels == 0]
    anomaly_scores = scores[labels == 1]

    # Plot histograms
    fig.add_trace(
        go.Histogram(x=normal_scores, name="Normal", opacity=0.7, nbinsx=30),
    )

    fig.add_trace(
        go.Histogram(x=anomaly_scores, name="Anomaly", opacity=0.7, nbinsx=30),
    )

    # Add threshold line if provided
    if threshold is not None:
        fig.add_vline(
            x=threshold,
            line_dash="dash",
            line_color="green",
            annotation_text="Threshold",
        )

    fig.update_layout(
        title=title,
        xaxis_title="Anomaly Score",
        yaxis_title="Frequency",
        height=height,
    )

    return fig
plot_performance_metrics staticmethod 
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plot_performance_metrics(
    metrics_dict: dict[str, float],
    title: str = "Performance Metrics",
    height: int = 400,
) -> go.Figure

Create performance metrics bar chart.

Parameters:

Name Type Description Default
metrics_dict dict[str, float]

Dictionary of metric names and values

 required
title str

Plot title

 'Performance Metrics'
height int

Plot height

 400

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_performance_metrics(
    metrics_dict: dict[str, float],
    title: str = "Performance Metrics",
    height: int = 400,
) -> go.Figure:
    """Create performance metrics bar chart.

    Args:
        metrics_dict: Dictionary of metric names and values
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    fig = go.Figure()

    metric_names = list(metrics_dict.keys())
    metric_values = list(metrics_dict.values())

    colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

    fig.add_trace(
        go.Bar(
            x=metric_names,
            y=metric_values,
            marker_color=colors[: len(metric_names)],
        ),
    )

    fig.update_layout(
        title=title,
        xaxis_title="Metrics",
        yaxis_title="Score",
        height=height,
    )

    return fig
plot_precision_recall_curve staticmethod 
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plot_precision_recall_curve(
    y_true: np.ndarray,
    y_scores: np.ndarray,
    title: str = "Precision-Recall Curve",
    height: int = 400,
) -> go.Figure

Create precision-recall curve.

Parameters:

Name Type Description Default
y_true ndarray

True labels

 required
y_scores ndarray

Prediction scores

 required
title str

Plot title

 'Precision-Recall Curve'
height int

Plot height

 400

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_precision_recall_curve(
    y_true: np.ndarray,
    y_scores: np.ndarray,
    title: str = "Precision-Recall Curve",
    height: int = 400,
) -> go.Figure:
    """Create precision-recall curve.

    Args:
        y_true: True labels
        y_scores: Prediction scores
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    # Calculate precision and recall for different thresholds
    thresholds = np.linspace(y_scores.min(), y_scores.max(), 100)
    precisions = []
    recalls = []

    for thresh in thresholds:
        y_pred = (y_scores > thresh).astype(int)
        if np.sum(y_pred) > 0:
            # Calculate precision and recall manually
            tp = np.sum((y_pred == 1) & (y_true == 1))
            fp = np.sum((y_pred == 1) & (y_true == 0))
            fn = np.sum((y_pred == 0) & (y_true == 1))

            prec = tp / (tp + fp) if (tp + fp) > 0 else 0
            rec = tp / (tp + fn) if (tp + fn) > 0 else 0

            precisions.append(prec)
            recalls.append(rec)
        else:
            precisions.append(0)
            recalls.append(0)

    fig = go.Figure()

    fig.add_trace(
        go.Scatter(
            x=recalls,
            y=precisions,
            mode="lines",
            name="PR Curve",
            line=dict(width=3),
        ),
    )

    fig.update_layout(
        title=title,
        xaxis_title="Recall",
        yaxis_title="Precision",
        height=height,
    )

    return fig
plot_roc_curve staticmethod 
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plot_roc_curve(
    y_true: np.ndarray,
    y_scores: np.ndarray,
    title: str = "ROC Curve",
    height: int = 400,
) -> go.Figure

Create ROC (Receiver Operating Characteristic) curve.

Parameters:

Name Type Description Default
y_true ndarray

True labels (binary: 0 or 1)

 required
y_scores ndarray

Prediction scores or probabilities

 required
title str

Plot title

 'ROC Curve'
height int

Plot height

 400

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_roc_curve(
    y_true: np.ndarray,
    y_scores: np.ndarray,
    title: str = "ROC Curve",
    height: int = 400,
) -> go.Figure:
    """Create ROC (Receiver Operating Characteristic) curve.

    Args:
        y_true: True labels (binary: 0 or 1)
        y_scores: Prediction scores or probabilities
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    # Calculate ROC curve for different thresholds
    thresholds = np.linspace(y_scores.max(), y_scores.min(), 100)
    tpr_list = []
    fpr_list = []

    for thresh in thresholds:
        y_pred = (y_scores > thresh).astype(int)

        # Calculate true positive rate and false positive rate
        tp = np.sum((y_pred == 1) & (y_true == 1))
        fp = np.sum((y_pred == 1) & (y_true == 0))
        fn = np.sum((y_pred == 0) & (y_true == 1))
        tn = np.sum((y_pred == 0) & (y_true == 0))

        tpr = tp / (tp + fn) if (tp + fn) > 0 else 0
        fpr = fp / (fp + tn) if (fp + tn) > 0 else 0

        tpr_list.append(tpr)
        fpr_list.append(fpr)

    # Calculate AUC (Area Under the Curve) using trapezoidal rule
    fpr_array = np.array(fpr_list)
    tpr_array = np.array(tpr_list)
    auc = np.trapz(tpr_array, fpr_array)

    fig = go.Figure()

    # Add ROC curve
    fig.add_trace(
        go.Scatter(
            x=fpr_list,
            y=tpr_list,
            mode="lines",
            name=f"ROC Curve (AUC = {auc:.3f})",
            line=dict(color="blue", width=3),
        ),
    )

    # Add diagonal reference line (random classifier)
    fig.add_trace(
        go.Scatter(
            x=[0, 1],
            y=[0, 1],
            mode="lines",
            name="Random Classifier",
            line=dict(color="red", dash="dash", width=2),
        ),
    )

    fig.update_layout(
        title=title,
        xaxis_title="False Positive Rate",
        yaxis_title="True Positive Rate",
        height=height,
        xaxis=dict(range=[0, 1]),
        yaxis=dict(range=[0, 1]),
    )

    return fig
plot_context_dependency staticmethod 
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plot_context_dependency(
    context_values: np.ndarray,
    accuracies: list[float],
    title: str = "Model Performance by Context",
    height: int = 400,
) -> go.Figure

Create context dependency plot.

Parameters:

Name Type Description Default
context_values ndarray

Context values or bin labels

 required
accuracies list[float]

Accuracies for each context bin

 required
title str

Plot title

 'Model Performance by Context'
height int

Plot height

 400

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_context_dependency(
    context_values: np.ndarray,
    accuracies: list[float],
    title: str = "Model Performance by Context",
    height: int = 400,
) -> go.Figure:
    """Create context dependency plot.

    Args:
        context_values: Context values or bin labels
        accuracies: Accuracies for each context bin
        title: Plot title
        height: Plot height

    Returns:
        Plotly figure
    """
    fig = go.Figure()

    if isinstance(context_values[0], (int, float)):
        x_labels = [f"Bin {i+1}" for i in range(len(context_values))]
    else:
        x_labels = list(context_values)

    fig.add_trace(go.Bar(x=x_labels, y=accuracies, marker_color="lightblue"))

    fig.update_layout(
        title=title,
        xaxis_title="Context Bins",
        yaxis_title="Accuracy",
        height=height,
    )

    return fig
create_comprehensive_plot staticmethod 
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create_comprehensive_plot(
    plot_type: str, **kwargs
) -> go.Figure

Create comprehensive plots with multiple subplots.

Parameters:

Name Type Description Default
plot_type str

Type of comprehensive plot ('anomaly_detection', 'classification', 'regression')

 required
**kwargs

Additional arguments for the specific plot type

 {}

Returns:

Type Description
Figure

Plotly figure
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def create_comprehensive_plot(plot_type: str, **kwargs) -> go.Figure:
    """Create comprehensive plots with multiple subplots.

    Args:
        plot_type: Type of comprehensive plot ('anomaly_detection', 'classification', 'regression')
        **kwargs: Additional arguments for the specific plot type

    Returns:
        Plotly figure
    """
    if plot_type == "anomaly_detection":
        return KerasFactoryPlotter._create_anomaly_detection_plot(**kwargs)
    elif plot_type == "classification":
        return KerasFactoryPlotter._create_classification_plot(**kwargs)
    elif plot_type == "regression":
        return KerasFactoryPlotter._create_regression_plot(**kwargs)
    else:
        raise ValueError(f"Unknown plot type: {plot_type}")
plot_timeseries staticmethod 
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plot_timeseries(
    X: np.ndarray,
    y_true: np.ndarray = None,
    y_pred: np.ndarray = None,
    n_samples_to_plot: int = 5,
    feature_idx: int = 0,
    title: str = "Time Series Forecast",
    height: int = 500,
) -> go.Figure

Plot time series data with optional predictions.

Parameters:

Name Type Description Default
X ndarray

Input sequences of shape (n_samples, seq_len, n_features).

 required
y_true ndarray

True target sequences of shape (n_samples, pred_len, n_features).

 None
y_pred ndarray

Predicted sequences of shape (n_samples, pred_len, n_features).

 None
n_samples_to_plot int

Number of samples to visualize.

 5
feature_idx int

Which feature to plot.

 0
title str

Plot title.

 'Time Series Forecast'
height int

Plot height.

 500

Returns:

Type Description
Figure

Plotly figure.
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_timeseries(
    X: np.ndarray,
    y_true: np.ndarray = None,
    y_pred: np.ndarray = None,
    n_samples_to_plot: int = 5,
    feature_idx: int = 0,
    title: str = "Time Series Forecast",
    height: int = 500,
) -> go.Figure:
    """Plot time series data with optional predictions.

    Args:
        X: Input sequences of shape (n_samples, seq_len, n_features).
        y_true: True target sequences of shape (n_samples, pred_len, n_features).
        y_pred: Predicted sequences of shape (n_samples, pred_len, n_features).
        n_samples_to_plot: Number of samples to visualize.
        feature_idx: Which feature to plot.
        title: Plot title.
        height: Plot height.

    Returns:
        Plotly figure.
    """
    fig = make_subplots(
        rows=n_samples_to_plot,
        cols=1,
        subplot_titles=[f"Sample {i+1}" for i in range(n_samples_to_plot)],
        vertical_spacing=0.05,
    )

    seq_len = X.shape[1]

    for sample_idx in range(min(n_samples_to_plot, len(X))):
        row = sample_idx + 1

        # Plot input sequence
        x_vals = list(range(seq_len))
        fig.add_trace(
            go.Scatter(
                x=x_vals,
                y=X[sample_idx, :, feature_idx],
                mode="lines",
                name="Input",
                line=dict(color="blue", width=2),
            ),
            row=row,
            col=1,
        )

        # Plot true target
        if y_true is not None:
            pred_len = y_true.shape[1]
            y_vals = list(range(seq_len, seq_len + pred_len))
            fig.add_trace(
                go.Scatter(
                    x=y_vals,
                    y=y_true[sample_idx, :, feature_idx],
                    mode="lines",
                    name="True",
                    line=dict(color="green", width=2),
                ),
                row=row,
                col=1,
            )

        # Plot predictions
        if y_pred is not None:
            pred_len = y_pred.shape[1]
            y_vals = list(range(seq_len, seq_len + pred_len))
            fig.add_trace(
                go.Scatter(
                    x=y_vals,
                    y=y_pred[sample_idx, :, feature_idx],
                    mode="lines",
                    name="Predicted",
                    line=dict(color="red", width=2, dash="dash"),
                ),
                row=row,
                col=1,
            )

    fig.update_layout(title=title, height=height, showlegend=True)
    fig.update_xaxes(title_text="Time Steps", row=n_samples_to_plot, col=1)
    fig.update_yaxes(
        title_text="Value",
        row=int((n_samples_to_plot + 1) / 2),
        col=1,
    )

    return fig
plot_timeseries_comparison staticmethod 
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plot_timeseries_comparison(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    sample_idx: int = 0,
    title: str = "Forecast Comparison",
    height: int = 400,
) -> go.Figure

Plot single time series forecast comparison.

Parameters:

Name Type Description Default
y_true ndarray

True sequences of shape (n_samples, pred_len, n_features) or (pred_len, n_features).

 required
y_pred ndarray

Predicted sequences of shape (n_samples, pred_len, n_features) or (pred_len, n_features).

 required
sample_idx int

Index of sample to plot (if 3D arrays).

 0
title str

Plot title.

 'Forecast Comparison'
height int

Plot height.

 400

Returns:

Type Description
Figure

Plotly figure.
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_timeseries_comparison(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    sample_idx: int = 0,
    title: str = "Forecast Comparison",
    height: int = 400,
) -> go.Figure:
    """Plot single time series forecast comparison.

    Args:
        y_true: True sequences of shape (n_samples, pred_len, n_features) or (pred_len, n_features).
        y_pred: Predicted sequences of shape (n_samples, pred_len, n_features) or (pred_len, n_features).
        sample_idx: Index of sample to plot (if 3D arrays).
        title: Plot title.
        height: Plot height.

    Returns:
        Plotly figure.
    """
    if len(y_true.shape) == 3:
        y_true = y_true[sample_idx]
    if len(y_pred.shape) == 3:
        y_pred = y_pred[sample_idx]

    fig = go.Figure()

    x_vals = list(range(len(y_true)))

    # For multivariate, plot first feature
    if len(y_true.shape) > 1:
        y_true_vals = y_true[:, 0]
        y_pred_vals = y_pred[:, 0]
    else:
        y_true_vals = y_true
        y_pred_vals = y_pred

    fig.add_trace(
        go.Scatter(
            x=x_vals,
            y=y_true_vals,
            mode="lines+markers",
            name="True",
            line=dict(color="green", width=2),
        ),
    )

    fig.add_trace(
        go.Scatter(
            x=x_vals,
            y=y_pred_vals,
            mode="lines+markers",
            name="Predicted",
            line=dict(color="red", width=2, dash="dash"),
        ),
    )

    fig.update_layout(
        title=title,
        xaxis_title="Time Steps",
        yaxis_title="Value",
        height=height,
    )

    return fig
plot_decomposition staticmethod 
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plot_decomposition(
    original: np.ndarray,
    trend: np.ndarray = None,
    seasonal: np.ndarray = None,
    residual: np.ndarray = None,
    title: str = "Time Series Decomposition",
    height: int = 600,
) -> go.Figure

Plot time series decomposition into components.

Parameters:

Name Type Description Default
original ndarray

Original time series.

 required
trend ndarray

Trend component.

 None
seasonal ndarray

Seasonal component.

 None
residual ndarray

Residual component.

 None
title str

Plot title.

 'Time Series Decomposition'
height int

Plot height.

 600

Returns:

Type Description
Figure

Plotly figure.
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_decomposition(
    original: np.ndarray,
    trend: np.ndarray = None,
    seasonal: np.ndarray = None,
    residual: np.ndarray = None,
    title: str = "Time Series Decomposition",
    height: int = 600,
) -> go.Figure:
    """Plot time series decomposition into components.

    Args:
        original: Original time series.
        trend: Trend component.
        seasonal: Seasonal component.
        residual: Residual component.
        title: Plot title.
        height: Plot height.

    Returns:
        Plotly figure.
    """
    components = {"Original": original}
    if trend is not None:
        components["Trend"] = trend
    if seasonal is not None:
        components["Seasonal"] = seasonal
    if residual is not None:
        components["Residual"] = residual

    n_components = len(components)
    fig = make_subplots(
        rows=n_components,
        cols=1,
        subplot_titles=list(components.keys()),
        vertical_spacing=0.08,
    )

    x_vals = list(range(len(original)))

    for i, (name, component) in enumerate(components.items()):
        row = i + 1
        fig.add_trace(
            go.Scatter(
                x=x_vals,
                y=component,
                mode="lines",
                name=name,
                line=dict(color=["blue", "green", "orange", "red"][i]),
            ),
            row=row,
            col=1,
        )

    fig.update_layout(title=title, height=height, showlegend=False)
    fig.update_yaxes(title_text="Value", row=int((n_components + 1) / 2), col=1)
    fig.update_xaxes(title_text="Time Steps", row=n_components, col=1)

    return fig
plot_forecasting_metrics staticmethod 
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plot_forecasting_metrics(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Forecasting Metrics",
    height: int = 400,
) -> go.Figure

Calculate and plot forecasting error metrics.

Parameters:

Name Type Description Default
y_true ndarray

True values.

 required
y_pred ndarray

Predicted values.

 required
title str

Plot title.

 'Forecasting Metrics'
height int

Plot height.

 400

Returns:

Type Description
Figure

Plotly figure with metrics.
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_forecasting_metrics(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Forecasting Metrics",
    height: int = 400,
) -> go.Figure:
    """Calculate and plot forecasting error metrics.

    Args:
        y_true: True values.
        y_pred: Predicted values.
        title: Plot title.
        height: Plot height.

    Returns:
        Plotly figure with metrics.
    """
    # Calculate errors
    mae = np.mean(np.abs(y_true - y_pred))
    rmse = np.sqrt(np.mean((y_true - y_pred) ** 2))
    mape = np.mean(np.abs((y_true - y_pred) / (np.abs(y_true) + 1e-8))) * 100

    metrics_dict = {"MAE": mae, "RMSE": rmse, "MAPE (%)": mape}

    return KerasFactoryPlotter.plot_performance_metrics(metrics_dict, title, height)
plot_forecast_horizon_analysis staticmethod 
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plot_forecast_horizon_analysis(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Forecast Error by Horizon",
    height: int = 400,
) -> go.Figure

Analyze forecast error across different forecast horizons.

Parameters:

Name Type Description Default
y_true ndarray

True sequences of shape (n_samples, pred_len) or (n_samples, pred_len, n_features).

 required
y_pred ndarray

Predicted sequences of same shape.

 required
title str

Plot title.

 'Forecast Error by Horizon'
height int

Plot height.

 400

Returns:

Type Description
Figure

Plotly figure.
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_forecast_horizon_analysis(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title: str = "Forecast Error by Horizon",
    height: int = 400,
) -> go.Figure:
    """Analyze forecast error across different forecast horizons.

    Args:
        y_true: True sequences of shape (n_samples, pred_len) or (n_samples, pred_len, n_features).
        y_pred: Predicted sequences of same shape.
        title: Plot title.
        height: Plot height.

    Returns:
        Plotly figure.
    """
    # Handle multivariate by taking first feature
    if len(y_true.shape) > 2:
        y_true = y_true[:, :, 0]
    if len(y_pred.shape) > 2:
        y_pred = y_pred[:, :, 0]

    pred_len = y_true.shape[1]
    mae_by_horizon = []

    for t in range(pred_len):
        mae = np.mean(np.abs(y_true[:, t] - y_pred[:, t]))
        mae_by_horizon.append(mae)

    fig = go.Figure()

    fig.add_trace(
        go.Scatter(
            x=list(range(1, pred_len + 1)),
            y=mae_by_horizon,
            mode="lines+markers",
            name="MAE",
            line=dict(color="blue", width=2),
        ),
    )

    fig.update_layout(
        title=title,
        xaxis_title="Forecast Horizon (steps ahead)",
        yaxis_title="Mean Absolute Error",
        height=height,
    )

    return fig
plot_multiple_features_forecast staticmethod 
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plot_multiple_features_forecast(
    X: np.ndarray,
    y_true: np.ndarray,
    y_pred: np.ndarray,
    sample_idx: int = 0,
    n_features_to_plot: int = None,
    title: str = "Multi-Feature Forecast",
    height: int = 500,
) -> go.Figure

Plot forecasts for multiple features side-by-side.

Parameters:

Name Type Description Default
X ndarray

Input sequences.

 required
y_true ndarray

True target sequences.

 required
y_pred ndarray

Predicted sequences.

 required
sample_idx int

Which sample to plot.

 0
n_features_to_plot int

Number of features to plot (default: all).

 None
title str

Plot title.

 'Multi-Feature Forecast'
height int

Plot height.

 500

Returns:

Type Description
Figure

Plotly figure.
Source code in kerasfactory/utils/plotting.py 
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@staticmethod
def plot_multiple_features_forecast(
    X: np.ndarray,
    y_true: np.ndarray,
    y_pred: np.ndarray,
    sample_idx: int = 0,
    n_features_to_plot: int = None,
    title: str = "Multi-Feature Forecast",
    height: int = 500,
) -> go.Figure:
    """Plot forecasts for multiple features side-by-side.

    Args:
        X: Input sequences.
        y_true: True target sequences.
        y_pred: Predicted sequences.
        sample_idx: Which sample to plot.
        n_features_to_plot: Number of features to plot (default: all).
        title: Plot title.
        height: Plot height.

    Returns:
        Plotly figure.
    """
    n_features = X.shape[2]
    if n_features_to_plot is None:
        n_features_to_plot = min(n_features, 4)

    seq_len = X.shape[1]
    pred_len = y_true.shape[1]

    fig = make_subplots(
        rows=1,
        cols=n_features_to_plot,
        subplot_titles=[f"Feature {i}" for i in range(n_features_to_plot)],
    )

    for feat_idx in range(n_features_to_plot):
        col = feat_idx + 1

        # Input
        x_vals = list(range(seq_len))
        fig.add_trace(
            go.Scatter(
                x=x_vals,
                y=X[sample_idx, :, feat_idx],
                mode="lines",
                name="Input",
                line=dict(color="blue"),
                showlegend=(feat_idx == 0),
            ),
            row=1,
            col=col,
        )

        # True target
        y_vals = list(range(seq_len, seq_len + pred_len))
        fig.add_trace(
            go.Scatter(
                x=y_vals,
                y=y_true[sample_idx, :, feat_idx],
                mode="lines",
                name="True",
                line=dict(color="green"),
                showlegend=(feat_idx == 0),
            ),
            row=1,
            col=col,
        )

        # Predicted
        fig.add_trace(
            go.Scatter(
                x=y_vals,
                y=y_pred[sample_idx, :, feat_idx],
                mode="lines",
                name="Predicted",
                line=dict(color="red", dash="dash"),
                showlegend=(feat_idx == 0),
            ),
            row=1,
            col=col,
        )

    fig.update_layout(title=title, height=height, showlegend=True)

    return fig

Time Series Plotting Methods

plot_timeseries()

Plot time series with input, true target, and predictions for multiple samples.

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from kerasfactory.utils import KerasFactoryPlotter

fig = KerasFactoryPlotter.plot_timeseries(
    X=X_test,
    y_true=y_test,
    y_pred=predictions,
    n_samples_to_plot=5,
    feature_idx=0,  # Which feature to plot
    title="Time Series Forecast"
)
fig.show()

Use When: Visualizing multiple forecast examples side-by-side to understand model behavior.

plot_timeseries_comparison()

Compare single forecast with true values.

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fig = KerasFactoryPlotter.plot_timeseries_comparison(
    y_true=y_test,
    y_pred=predictions,
    sample_idx=0,
    title="Forecast Comparison"
)
fig.show()

Use When: Detailed analysis of a single sample forecast.

plot_decomposition()

Visualize time series decomposition into components (trend, seasonal, residual).

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fig = KerasFactoryPlotter.plot_decomposition(
    original=time_series,
    trend=trend_component,
    seasonal=seasonal_component,
    residual=residual_component,
    title="Time Series Decomposition"
)
fig.show()

Use When: Understanding component contributions in time series models.

plot_forecasting_metrics()

Calculate and display MAE, RMSE, and MAPE metrics.

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fig = KerasFactoryPlotter.plot_forecasting_metrics(
    y_true=y_test,
    y_pred=predictions,
    title="Forecasting Performance"
)
fig.show()

Use When: Quick performance overview of forecasting model.

plot_forecast_horizon_analysis()

Analyze forecast error across different forecast horizons (how far ahead).

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fig = KerasFactoryPlotter.plot_forecast_horizon_analysis(
    y_true=y_test,
    y_pred=predictions,
    title="Error by Forecast Horizon"
)
fig.show()

Use When: Understanding if model degrades for longer forecasts.

plot_multiple_features_forecast()

Plot forecasts for multiple features side-by-side.

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fig = KerasFactoryPlotter.plot_multiple_features_forecast(
    X=X_test,
    y_true=y_test,
    y_pred=predictions,
    sample_idx=0,
    n_features_to_plot=4,
    title="Multi-Feature Forecast"
)
fig.show()

Use When: Comparing forecast quality across multiple time series channels.

Training & Metrics Methods

plot_training_history()

Visualize training and validation metrics over epochs.

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fig = KerasFactoryPlotter.plot_training_history(
    history=model.history,
    metrics=['loss', 'mae', 'accuracy'],
    title="Training Progress"
)
fig.show()
plot_confusion_matrix()

Heatmap of classification confusion matrix.

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fig = KerasFactoryPlotter.plot_confusion_matrix(
    y_true=y_test,
    y_pred=y_pred_labels,
    title="Confusion Matrix"
)
fig.show()
plot_roc_curve()

ROC curve with AUC score.

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fig = KerasFactoryPlotter.plot_roc_curve(
    y_true=y_test,
    y_scores=y_pred_probs,
    title="ROC Curve"
)
fig.show()
plot_precision_recall_curve()

Precision-recall curve visualization.

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fig = KerasFactoryPlotter.plot_precision_recall_curve(
    y_true=y_test,
    y_scores=y_pred_probs,
    title="Precision-Recall Curve"
)
fig.show()
plot_anomaly_scores()

Distribution of anomaly scores with threshold visualization.

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fig = KerasFactoryPlotter.plot_anomaly_scores(
    scores=anomaly_scores,
    labels=true_labels,
    threshold=5.0,
    title="Anomaly Scores"
)
fig.show()
plot_performance_metrics()

Bar chart of performance metrics.

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metrics = {
    "Accuracy": 0.95,
    "Precision": 0.92,
    "Recall": 0.88,
    "F1": 0.90
}

fig = KerasFactoryPlotter.plot_performance_metrics(metrics)
fig.show()

🛠️ Decorators

✨ Decorators

Utility decorators for common functionality in KerasFactory components and enhanced development experience.

kerasfactory.utils.decorators

Functions

log_init 
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log_init(cls: type[T]) -> type[T]

Class decorator to log initialization arguments.

Source code in kerasfactory/utils/decorators.py 
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def log_init(cls: type[T]) -> type[T]:
    """Class decorator to log initialization arguments."""
    original_init = cls.__init__  # type: ignore

    @functools.wraps(original_init)
    def new_init(self: Any, *args: Any, **kwargs: Any) -> None:
        # Convert input_schema to regular dict if present
        if "input_schema" in kwargs:
            kwargs["input_schema"] = dict(kwargs["input_schema"])

        # Get the signature of the original __init__
        sig = inspect.signature(original_init)
        bound_args = sig.bind(self, *args, **kwargs)
        bound_args.apply_defaults()

        # Remove 'self' from the arguments
        init_args = dict(bound_args.arguments)
        init_args.pop("self", None)

        # Store arguments for potential later use
        self._init_args = init_args

        # Separate args and kwargs based on parameter kinds
        required_args = []
        optional_kwargs = {}

        for name, param in sig.parameters.items():
            if name == "self":
                continue

            value = init_args.get(name)
            if param.default == inspect.Parameter.empty:
                required_args.append(f"{name}={value}")
            else:
                # Only include kwargs that differ from their defaults
                if value != param.default:
                    optional_kwargs[name] = value

        # Format and log the initialization message
        class_name = cls.__name__
        args_str = ", ".join(required_args)
        kwargs_str = ", ".join([f"{k}={v}" for k, v in optional_kwargs.items()])

        if kwargs_str:
            logger.info(
                f"Initializing {class_name} with args: ({args_str}) and kwargs: ({kwargs_str})",
            )
        else:
            logger.info(f"Initializing {class_name} with args: ({args_str})")

        # Call the original __init__
        original_init(self, *args, **kwargs)

    cls.__init__ = new_init  # type: ignore
    return cls
log_method 
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log_method(func: Callable) -> Callable

Method decorator to log method calls with their arguments.

Source code in kerasfactory/utils/decorators.py 
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def log_method(func: Callable) -> Callable:
    """Method decorator to log method calls with their arguments."""

    @functools.wraps(func)
    def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
        # Convert input dictionaries to regular dicts
        new_args = []
        for arg in args:
            if isinstance(arg, dict):
                new_args.append(dict(arg))
            else:
                new_args.append(arg)

        new_kwargs = {}
        for key, value in kwargs.items():
            if isinstance(value, dict):
                new_kwargs[key] = dict(value)
            else:
                new_kwargs[key] = value

        # Get the signature of the function
        sig = inspect.signature(func)
        bound_args = sig.bind(self, *new_args, **new_kwargs)
        bound_args.apply_defaults()

        # Remove 'self' from the arguments
        call_args = dict(bound_args.arguments)
        call_args.pop("self", None)

        # Format the log message
        method_name = func.__name__
        args_str = ", ".join([f"args={new_args}"] if new_args else [])
        kwargs_str = ", ".join([f"{k}={v}" for k, v in new_kwargs.items()])

        if args_str and kwargs_str:
            logger.info(f"Calling {method_name} with {args_str}, {kwargs_str}")
        elif args_str:
            logger.info(f"Calling {method_name} with {args_str}")
        elif kwargs_str:
            logger.info(f"Calling {method_name} with {kwargs_str}")
        else:
            logger.info(f"Calling {method_name} with ()")

        # Call the original function
        result = func(self, *new_args, **new_kwargs)
        return result

    return wrapper
log_property 
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log_property(func: Callable) -> Callable

Property decorator to log property access.

Source code in kerasfactory/utils/decorators.py 
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def log_property(func: Callable) -> Callable:
    """Property decorator to log property access."""

    @functools.wraps(func)
    def wrapper(self: Any) -> Any:
        property_name = func.__name__
        logger.debug(f"Accessing property {property_name}")
        return func(self)

    return wrapper
add_serialization 
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add_serialization(cls: T) -> T

Decorator to add serialization methods to a Keras model class.

Parameters:

Name Type Description Default
cls T

The class to decorate.

 required

Returns:

Type Description
T

The decorated class.
Source code in kerasfactory/utils/decorators.py 
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def add_serialization(cls: T) -> T:
    """Decorator to add serialization methods to a Keras model class.

    Args:
        cls: The class to decorate.

    Returns:
        The decorated class.
    """
    # Register the class for Keras serialization
    cls = register_keras_serializable()(cls)

    original_init = cls.__init__

    @functools.wraps(original_init)
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        """Initialize the decorator.

        Args:
            self: The instance being initialized.
            *args: Provided class arguments.
            **kwargs: Provided kwargs for the class.

        """
        # Bind the arguments to get a dictionary of the parameters
        sig = inspect.signature(original_init)
        bound_args = sig.bind(self, *args, **kwargs)
        bound_args.apply_defaults()
        init_args = dict(bound_args.arguments)
        init_args.pop("self", None)

        # Store the initialization arguments
        self._init_args = init_args

        # Call the original __init__ method
        original_init(self, *args, **kwargs)

    def get_config(self) -> dict[str, Any]:
        """Return the configuration of the model.

        Returns:
            dict: serializable configuration of the class.
        """
        base_config = super().get_config()  # type: ignore
        return {**base_config, **self._init_args}

    @classmethod  # type: ignore
    def from_config(cls, config: dict[str, Any]) -> Any:
        """Create an instance from a configuration dictionary.

        Args:
            cls: The class being instantiated.
            config: Configuration dictionary for deserialization.
        """
        return cls(**config)

    # Assign the new methods to the class
    cls.__init__ = __init__
    cls.get_config = get_config
    cls.from_config = from_config

    return cls

📚 Complete Example

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from kerasfactory.utils import KerasFactoryDataGenerator, KerasFactoryPlotter
from kerasfactory.models import TSMixer
import keras

# 1. Generate synthetic time series data
X_train, y_train = KerasFactoryDataGenerator.generate_seasonal_timeseries(
    n_samples=500, seq_len=96, pred_len=12, n_features=7
)
X_test, y_test = KerasFactoryDataGenerator.generate_seasonal_timeseries(
    n_samples=100, seq_len=96, pred_len=12, n_features=7
)

# 2. Create model
model = TSMixer(seq_len=96, pred_len=12, n_features=7)
model.compile(optimizer='adam', loss='mse')

# 3. Train model
history = model.fit(X_train, y_train, validation_split=0.2, epochs=10)

# 4. Visualize training
fig = KerasFactoryPlotter.plot_training_history(history, metrics=['loss'])
fig.show()

# 5. Make predictions
predictions = model.predict(X_test)

# 6. Visualize forecasts
fig = KerasFactoryPlotter.plot_timeseries(
    X_test, y_test, predictions, n_samples_to_plot=3
)
fig.show()

# 7. Analyze performance
fig = KerasFactoryPlotter.plot_forecasting_metrics(y_test, predictions)
fig.show()

# 8. Detailed analysis
fig = KerasFactoryPlotter.plot_forecast_horizon_analysis(y_test, predictions)
fig.show()

🎯 Best Practices

  1. Always use KerasFactoryDataGenerator for synthetic data in notebooks
  2. Leverage KerasFactoryPlotter for consistent visualizations across projects
  3. Create TensorFlow datasets with create_timeseries_dataset() for efficient training
  4. Use semantic data generation methods (e.g., generate_seasonal_timeseries()) that match your use case
  5. Chain visualizations to tell a complete story about model performance

📦 Testing

All utilities are thoroughly tested. Run tests with:

1
pytest tests/utils/ -v

Test coverage includes: - ✓ Time series generation with various configurations - ✓ Data distribution validation
- ✓ Plotting function robustness with edge cases - ✓ Different data shapes and dimensions - ✓ Error handling and validation