Model Utilities

Examples

Examples of using the Model Utilities are listed at the bottom of this page Examples.

Model Utilities for Workbench models

instance_architecture(instance_name)

Get the architecture for the given instance name

Source code in src/workbench/utils/model_utils.py

def instance_architecture(instance_name: str) -> str:
    """Get the architecture for the given instance name"""
    info = model_instance_info()
    return info[info["Instance Name"] == instance_name]["Architecture"].values[0]

load_category_mappings_from_s3(model_artifact_uri)

Download and extract category mappings from a model artifact in S3.

Parameters:

Name	Type	Description	Default
model_artifact_uri	str	S3 URI of the model artifact.	required

Returns:

Name	Type	Description
dict	Optional[dict]	The loaded category mappings or None if not found.

Source code in src/workbench/utils/model_utils.py

def load_category_mappings_from_s3(model_artifact_uri: str) -> Optional[dict]:
    """
    Download and extract category mappings from a model artifact in S3.

    Args:
        model_artifact_uri (str): S3 URI of the model artifact.

    Returns:
        dict: The loaded category mappings or None if not found.
    """
    category_mappings = None

    with tempfile.TemporaryDirectory() as tmpdir:
        # Download model artifact
        local_tar_path = os.path.join(tmpdir, "model.tar.gz")
        wr.s3.download(path=model_artifact_uri, local_file=local_tar_path)

        # Extract tarball
        with tarfile.open(local_tar_path, "r:gz") as tar:
            tar.extractall(path=tmpdir, filter="data")

        # Look for category mappings in base directory only
        mappings_path = os.path.join(tmpdir, "category_mappings.json")

        if os.path.exists(mappings_path):
            try:
                with open(mappings_path, "r") as f:
                    category_mappings = json.load(f)
                print(f"Loaded category mappings from {mappings_path}")
            except Exception as e:
                print(f"Failed to load category mappings from {mappings_path}: {e}")

    return category_mappings

model_instance_info()

Get the instance information for the Model

Source code in src/workbench/utils/model_utils.py

def model_instance_info() -> pd.DataFrame:
    """Get the instance information for the Model"""
    data = [
        {
            "Instance Name": "ml.t2.medium",
            "vCPUs": 2,
            "Memory": 4,
            "Price per Hour": 0.06,
            "Category": "General",
            "Architecture": "x86_64",
        },
        {
            "Instance Name": "ml.m7i.large",
            "vCPUs": 2,
            "Memory": 8,
            "Price per Hour": 0.12,
            "Category": "General",
            "Architecture": "x86_64",
        },
        {
            "Instance Name": "ml.c7i.large",
            "vCPUs": 2,
            "Memory": 4,
            "Price per Hour": 0.11,
            "Category": "Compute",
            "Architecture": "x86_64",
        },
        {
            "Instance Name": "ml.c7i.xlarge",
            "vCPUs": 4,
            "Memory": 8,
            "Price per Hour": 0.21,
            "Category": "Compute",
            "Architecture": "x86_64",
        },
        {
            "Instance Name": "ml.c7g.large",
            "vCPUs": 2,
            "Memory": 4,
            "Price per Hour": 0.09,
            "Category": "Compute",
            "Architecture": "arm64",
        },
        {
            "Instance Name": "ml.c7g.xlarge",
            "vCPUs": 4,
            "Memory": 8,
            "Price per Hour": 0.17,
            "Category": "Compute",
            "Architecture": "arm64",
        },
    ]
    return pd.DataFrame(data)

proximity_model(model, prox_model_name, track_columns=None)

Create a proximity model based on the given model

Parameters:

Name	Type	Description	Default
model	Model	The model to create the proximity model from	required
prox_model_name	str	The name of the proximity model to create	required
track_columns	list	List of columns to track in the proximity model	None

Returns: Model: The proximity model

Source code in src/workbench/utils/model_utils.py

def proximity_model(model: "Model", prox_model_name: str, track_columns: list = None) -> "Model":
    """Create a proximity model based on the given model

    Args:
        model (Model): The model to create the proximity model from
        prox_model_name (str): The name of the proximity model to create
        track_columns (list, optional): List of columns to track in the proximity model
    Returns:
        Model: The proximity model
    """
    from workbench.api import Model, ModelType, FeatureSet  # noqa: F401 (avoid circular import)

    # Get the custom script path for the proximity model
    script_path = get_custom_script_path("proximity", "feature_space_proximity.template")

    # Get Feature and Target Columns from the existing given Model
    features = model.features()
    target = model.target()

    # Create the Proximity Model from our FeatureSet
    fs = FeatureSet(model.get_input())
    prox_model = fs.to_model(
        name=prox_model_name,
        model_type=ModelType.PROXIMITY,
        feature_list=features,
        target_column=target,
        description=f"Proximity Model for {model.name}",
        tags=["proximity", model.name],
        custom_script=script_path,
        custom_args={"track_columns": track_columns},
    )
    return prox_model

supported_instance_types(arch='x86_64')

Get the supported instance types for the Model/Model

Source code in src/workbench/utils/model_utils.py

def supported_instance_types(arch: str = "x86_64") -> list:
    """Get the supported instance types for the Model/Model"""

    # Filter the instance types based on the architecture
    info = model_instance_info()
    return info[info["Architecture"] == arch]["Instance Name"].tolist()

uq_metrics(df, target_col)

Evaluate uncertainty quantification model with essential metrics. Args: df: DataFrame with predictions and uncertainty estimates. Must contain the target column, a prediction column ("prediction"), and either quantile columns ("q_025", "q_975", "q_25", "q_75") or a standard deviation column ("prediction_std"). target_col: Name of the true target column in the DataFrame. Returns: Dictionary of computed metrics.

Source code in src/workbench/utils/model_utils.py

def uq_metrics(df: pd.DataFrame, target_col: str) -> Dict[str, Any]:
    """
    Evaluate uncertainty quantification model with essential metrics.
    Args:
        df: DataFrame with predictions and uncertainty estimates.
            Must contain the target column, a prediction column ("prediction"), and either
            quantile columns ("q_025", "q_975", "q_25", "q_75") or a standard deviation
            column ("prediction_std").
        target_col: Name of the true target column in the DataFrame.
    Returns:
        Dictionary of computed metrics.
    """
    # Input Validation
    if df.empty:
        raise ValueError("Input DataFrame is empty.")
    if target_col not in df.columns:
        raise ValueError(f"Target column '{target_col}' not found in DataFrame.")
    if "prediction" not in df.columns:
        raise ValueError("Prediction column 'prediction' not found in DataFrame.")

    # --- Coverage and Interval Width ---
    if "q_025" in df.columns and "q_975" in df.columns:
        lower_95, upper_95 = df["q_025"], df["q_975"]
        lower_90, upper_90 = df["q_05"], df["q_95"]
        lower_80, upper_80 = df["q_10"], df["q_90"]
        lower_50, upper_50 = df["q_25"], df["q_75"]
    elif "prediction_std" in df.columns:
        lower_95 = df["prediction"] - 1.96 * df["prediction_std"]
        upper_95 = df["prediction"] + 1.96 * df["prediction_std"]
        lower_50 = df["prediction"] - 0.674 * df["prediction_std"]
        upper_50 = df["prediction"] + 0.674 * df["prediction_std"]
    else:
        raise ValueError(
            "Either quantile columns (q_025, q_975, q_25, q_75) or 'prediction_std' column must be present."
        )
    coverage_95 = np.mean((df[target_col] >= lower_95) & (df[target_col] <= upper_95))
    coverage_90 = np.mean((df[target_col] >= lower_90) & (df[target_col] <= upper_90))
    coverage_80 = np.mean((df[target_col] >= lower_80) & (df[target_col] <= upper_80))
    coverage_50 = np.mean((df[target_col] >= lower_50) & (df[target_col] <= upper_50))
    avg_width_95 = np.mean(upper_95 - lower_95)
    avg_width_90 = np.mean(upper_90 - lower_90)
    avg_width_80 = np.mean(upper_80 - lower_80)
    avg_width_50 = np.mean(upper_50 - lower_50)

    # --- CRPS (measures calibration + sharpness) ---
    if "prediction_std" in df.columns:
        z = (df[target_col] - df["prediction"]) / df["prediction_std"]
        crps = df["prediction_std"] * (z * (2 * norm.cdf(z) - 1) + 2 * norm.pdf(z) - 1 / np.sqrt(np.pi))
        mean_crps = np.mean(crps)
    else:
        mean_crps = np.nan

    # --- Interval Score @ 95% (penalizes miscoverage) ---
    alpha_95 = 0.05
    is_95 = (
        (upper_95 - lower_95)
        + (2 / alpha_95) * (lower_95 - df[target_col]) * (df[target_col] < lower_95)
        + (2 / alpha_95) * (df[target_col] - upper_95) * (df[target_col] > upper_95)
    )
    mean_is_95 = np.mean(is_95)

    # --- Adaptive Calibration (correlation between errors and uncertainty) ---
    abs_residuals = np.abs(df[target_col] - df["prediction"])
    width_95 = upper_95 - lower_95
    adaptive_calibration = np.corrcoef(abs_residuals, width_95)[0, 1]

    # Collect results
    results = {
        "coverage_95": coverage_95,
        "coverage_90": coverage_90,
        "coverage_80": coverage_80,
        "coverage_50": coverage_50,
        "avg_width_95": avg_width_95,
        "avg_width_50": avg_width_50,
        "crps": mean_crps,
        "interval_score_95": mean_is_95,
        "adaptive_calibration": adaptive_calibration,
        "n_samples": len(df),
    }

    print("\n=== UQ Metrics ===")
    print(f"Coverage @ 95%: {coverage_95:.3f} (target: 0.95)")
    print(f"Coverage @ 90%: {coverage_90:.3f} (target: 0.90)")
    print(f"Coverage @ 80%: {coverage_80:.3f} (target: 0.80)")
    print(f"Coverage @ 50%: {coverage_50:.3f} (target: 0.50)")
    print(f"Average 95% Width: {avg_width_95:.3f}")
    print(f"Average 90% Width: {avg_width_90:.3f}")
    print(f"Average 80% Width: {avg_width_80:.3f}")
    print(f"Average 50% Width: {avg_width_50:.3f}")
    print(f"CRPS: {mean_crps:.3f} (lower is better)")
    print(f"Interval Score 95%: {mean_is_95:.3f} (lower is better)")
    print(f"Adaptive Calibration: {adaptive_calibration:.3f} (higher is better, target: >0.5)")
    print(f"Samples: {len(df)}")
    return results

uq_model(model, uq_model_name, train_all_data=False)

Create a Uncertainty Quantification (UQ) model based on the given model

Parameters:

Name	Type	Description	Default
model	Model	The model to create the UQ model from	required
uq_model_name	str	The name of the UQ model to create	required
train_all_data	bool	Whether to train the UQ model on all data (default: False)	False

Returns:

Name	Type	Description
Model	Model	The UQ model

Source code in src/workbench/utils/model_utils.py

def uq_model(model: "Model", uq_model_name: str, train_all_data: bool = False) -> "Model":
    """Create a Uncertainty Quantification (UQ) model based on the given model

    Args:
        model (Model): The model to create the UQ model from
        uq_model_name (str): The name of the UQ model to create
        train_all_data (bool, optional): Whether to train the UQ model on all data (default: False)

    Returns:
        Model: The UQ model
    """
    from workbench.api import Model, ModelType, FeatureSet  # noqa: F401 (avoid circular import)

    # Get the custom script path for the UQ model
    script_path = get_custom_script_path("uq_models", "meta_uq.template")

    # Get Feature and Target Columns from the existing given Model
    features = model.features()
    target = model.target()

    # Create the Proximity Model from our FeatureSet
    fs = FeatureSet(model.get_input())
    uq_model = fs.to_model(
        name=uq_model_name,
        model_type=ModelType.UQ_REGRESSOR,
        feature_list=features,
        target_column=target,
        description=f"UQ Model for {model.name}",
        tags=["uq", model.name],
        train_all_data=train_all_data,
        custom_script=script_path,
        custom_args={"id_column": fs.id_column, "track_columns": [target]},
    )
    return uq_model

Examples

Feature Importance

"""Example for using some Model Utilities"""
from workbench.utils.model_utils import feature_importance

model = Model("aqsol_classification")
feature_importance(model)

Output

[('mollogp', 469.0),
 ('minabsestateindex', 277.0),
 ('peoe_vsa8', 237.0),
 ('qed', 237.0),
 ('fpdensitymorgan1', 230.0),
 ('fpdensitymorgan3', 221.0),
 ('estate_vsa4', 220.0),
 ('bcut2d_logphi', 218.0),
 ('vsa_estate5', 218.0),
 ('vsa_estate4', 209.0),

Additional Resources

• Workbench API Classes: API Classes
• Consulting Available: SuperCowPowers LLC