# Abacus Core Mixins: Diagnostics (`diagnostics.py`)

This module provides the `DiagnosticsMixin` class, intended to be inherited by Marketing Mix Model (MMM) classes in Abacus. It offers methods for assessing model fit and predictive performance using standard Bayesian diagnostic tools.

::: abacus.core.mixins.diagnostics

## `DiagnosticsMixin` Class

This mixin assumes the inheriting class has an `idata` attribute which is an ArviZ `InferenceData` object containing the results of the model fitting process (including the posterior and log-likelihood).

### Methods

#### `compute_elpd`

```python
def compute_elpd(self, model_name: str = "MMM", file_path: str = "ELPD.txt"):
```

Computes the Expected Log Pointwise Predictive Density (ELPD) using Leave-One-Out Cross-Validation (LOO-CV).

ELPD is a measure of a model's out-of-sample predictive accuracy. Higher values generally indicate better predictive performance. This method uses ArviZ's `az.loo()` function to perform the calculation based on the model's `idata`.

The computed ELPD value and the detailed summary provided by `az.loo()` (which includes estimates of the effective number of parameters `p_loo` and warnings about Pareto k diagnostic values) are saved to the specified text file.

**Parameters:**

-   `model_name` (`str`, optional): A name for the model being evaluated, included in the output file. Defaults to `"MMM"`.
-   `file_path` (`str`, optional): The path to the output text file where the ELPD results will be saved. Defaults to `"ELPD.txt"` in the current working directory. The directory will be created if it doesn't exist.

**Raises:**

-   `RuntimeError`: If the model has not been fitted yet (i.e., `self.idata` is `None` or does not contain posterior samples).

**Notes:**

-   LOO-CV can be computationally intensive, especially for large datasets or complex models.
-   The output file contains the ELPD point estimate and the full ArviZ LOO summary, which should be inspected for potential issues indicated by high Pareto k values.