Model Evaluators#

class MeasureEvaluator(method_name: str, description: str)[source]#

Bases: BaseEvaluator

Template for evaluators that calculate measures of model performance.

Abstract base class for evaluators that calculate performance measures and metrics for machine learning models. Provides a standardized workflow for model evaluation including prediction generation, metric calculation, metadata handling, and result saving.

Parameters:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output

Attributes:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
servicesServiceBundle or None: The global services bundle
metric_configMetricManager or None: The metric configuration manager
primary_colorstr: Primary color for plots and visualizations
secondary_colorstr: Secondary color for plots and visualizations
accent_colorstr: Accent color for plots and visualizations

Notes

This abstract base class provides a template for implementing model performance evaluation methods. Subclasses must implement the _calculate_measures method to define the specific metric calculation logic.

The class handles the complete evaluation workflow: 1. Generate predictions using the model 2. Calculate measures using _calculate_measures method 3. Generate metadata for the results 4. Save results to JSON file with metadata 5. Log the results

Examples

Create a custom measure evaluator:

>>> class CustomMeasureEvaluator(MeasureEvaluator):
...     def __init__(self):
...         super().__init__("custom_measure", "a custom measure")
...     
...     def _calculate_measures(self, predictions, y_true, metrics):
...         # Custom measure calculation logic
...         return {"custom_metric": 0.85}

evaluate(model: BaseEstimator, X: DataFrame, y: Series, metrics: List[str], filename: str) → None[source]#

Template for all measure methods to follow.

Executes the complete evaluation workflow for model performance measures. This method orchestrates the evaluation process by generating predictions, calculating measures, and handling result processing.

Parameters:

modelbase.BaseEstimator: The trained model to evaluate
Xpd.DataFrame: The input data for evaluation
ypd.Series: The true target values
metricsList[str]: The list of metric names to calculate
filenamestr: The name of the file to save the results to (without extension)

Returns:

None

Notes

This method provides the standard workflow for model evaluation: 1. Generate predictions using the model 2. Calculate measures using _calculate_measures 3. Generate metadata for the results 4. Save results to JSON file 5. Log the results

The method delegates the actual measure calculation to the _calculate_measures method, which must be implemented by subclasses.

report(results: Dict[str, Any]) → Tuple[List[str], List[List[Any]]][source]#

Default reporting - can be overridden.

Converts evaluation results into a format suitable for reporting. By default, assumes that the keys of the results dictionary are column headers and values are lists corresponding to each row.

Parameters:

resultsDict[str, Any]: The results of the evaluation

Returns:

Tuple[List[str], List[List[Any]]]: A tuple containing: - List of column headers - Nested list of rows (each row is a list of values)

Notes

The default implementation assumes a specific results format where keys are column headers and values are lists of row data. If the results are in a different format, this method should be overridden to return the appropriate column headers and row data.

The metadata key is automatically excluded from the column headers.

class PlotEvaluator(method_name: str, description: str, plot_settings)[source]#

Bases: BaseEvaluator

Template for model evaluators that plot data.

Abstract base class for evaluators that create plots and visualizations from model predictions and data. Provides a standardized workflow for model plotting including plot data generation, plot creation, metadata handling, and result saving.

Parameters:

method_namestr: The name of the evaluation method
descriptionstr: The description of the evaluation method
plot_settingsPlotSettings: The plot settings containing theme and color configuration

Attributes:

method_namestr: The name of the evaluator
descriptionstr: The description of the evaluator output
themeAny: The plot theme for styling plots
primary_colorstr: Primary color for plots (from plot settings)
secondary_colorstr: Secondary color for plots (from plot settings)
accent_colorstr: Accent color for plots (from plot settings)
servicesServiceBundle or None: The global services bundle (inherited from BaseEvaluator)
metric_configMetricManager or None: The metric configuration manager (inherited from BaseEvaluator)

Notes

This abstract base class provides a template for implementing model plotting methods. Subclasses must implement the _generate_plot_data and _create_plot methods to define the specific plotting logic.

The class handles the complete plotting workflow: 1. Generate plot data using _generate_plot_data method 2. Create the plot using _create_plot method 3. Generate metadata for the plot 4. Save the plot with metadata 5. Log the results

The constructor automatically configures matplotlib to use a non-interactive backend for thread safety and applies the provided plot settings.

Examples

Create a custom plot evaluator:

>>> class CustomPlotEvaluator(PlotEvaluator):
...     def __init__(self, plot_settings):
...         super().__init__("custom", "Custom plot", plot_settings)
...     
...     def _generate_plot_data(self, model, X, y, **kwargs):
...         # Custom plot data generation logic
...         return plot_data
...     
...     def _create_plot(self, plot_data, **kwargs):
...         # Custom plot creation logic
...         return plot

plot(model: BaseEstimator, X: DataFrame, y: Series, filename: str) → None[source]#

Template for all plot methods to follow.

Executes the complete plotting workflow for model plots. This method orchestrates the plotting process by calling the abstract methods and handling plot processing.

Parameters:

modelbase.BaseEstimator: The trained model to evaluate
Xpd.DataFrame: The input data for plotting
ypd.Series: The true target values
filenamestr: The name of the file to save the plot to (without extension)

Returns:

None

Notes

This method provides the standard workflow for model plotting: 1. Generate plot data using _generate_plot_data 2. Create the plot using _create_plot 3. Generate metadata for the plot 4. Save the plot with metadata 5. Log the results

The method delegates the actual plot data generation and plot creation to the abstract methods, which must be implemented by subclasses.

Model Evaluators#

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