## gallery.GalleryEntry


Usage

``` python
gallery.GalleryEntry()
```


## Methods

| Name | Description |
|----|----|
| [card()](#card) | The model card (card.md next to the entry's module). |
| [cohort_index()](#cohort_index) | Which of [cohorts()](gallery.GalleryEntry.md#ibnr.gallery.GalleryEntry.cohorts) a `segment=` argument names, or None. |
| [cohorts()](#cohorts) | Every cohort this fit answers for, in [predict()](gallery.GalleryEntry.md#ibnr.gallery.GalleryEntry.predict)'s target order. |
| [evaluate()](#evaluate) | Score realized outcomes against the predictive distribution. |
| [fit()](#fit) | Fit on a training triangle (already sliced with as_of). |
| [predict()](#predict) | Predictive distribution of the fitted quantities (e.g. ultimates). |
| [realized_ultimates()](#realized_ultimates) | Outcomes aligned, element for element, to `predict(segment)`'s targets. |

------------------------------------------------------------------------


#### card()


The model card (card.md next to the entry's module).


Usage

``` python
card()
```


------------------------------------------------------------------------


#### cohort_index()


Which of [cohorts()](gallery.GalleryEntry.md#ibnr.gallery.GalleryEntry.cohorts) a `segment=` argument names, or None.


Usage

``` python
cohort_index(segment)
```


`None` in, `None` out - the entry then does whatever `segment=None` means for it. Otherwise every supplied pair must match and exactly one cohort must survive: `segment` is a **filter on this fit's cohorts**, not a description of a triangle. A subset that identifies one cohort is accepted, which is what lets one loop pass the same dict to a fit keyed on `(company_code, company_name, line_of_business)` and to a pooled fit keyed on `(company_code, line_of_business)`.

Implemented once here and used by all 15 entries, which is why a mismatch reports the same way everywhere: naming the fit's own cohort key, and never silently scoring the fitted cohort for a typo'd one.


------------------------------------------------------------------------


#### cohorts()


Every cohort this fit answers for, in [predict()](gallery.GalleryEntry.md#ibnr.gallery.GalleryEntry.predict)'s target order.


Usage

``` python
cohorts()
```


Length 1 for a single-cohort fit, one entry per pooled cohort otherwise, so `for seg in entry.cohorts(): entry.predict(segment=seg)` loops over any entry in the gallery with no knowledge of its family.

Each dict is the cohort's FULL segment identity as the triangle carried it - **including any column the fit's own cohort KEY does not carry** (`kernels.nn_contract.DISPLAY_COLUMNS`: a pooled NN fit is keyed on `company_code` and drops `company_name`, so that two spellings of one company do not become two cohorts). A caller must not be told a cohort is unidentifiable when the fit knows exactly which one it is.

Abstract rather than defaulted to `contract_["segment"]`: a pooled entry that forgot to override would then get a plausible answer from a key that happens to exist, which is the entries-cloned-from-stale-templates failure. `register()` refuses an entry that skips it.


------------------------------------------------------------------------


#### evaluate()


Score realized outcomes against the predictive distribution.


Usage

``` python
evaluate(observed, segment=None)
```


Default implementation (kernels-backed): the Meyers-style summary table plus the outcome percentile of each target. Richer harnesses (ELPD, stacking) extend this in kernels, not in entries.

`segment` is passed straight to [predict()](gallery.GalleryEntry.md#ibnr.gallery.GalleryEntry.predict), so it selects exactly the same cohort here as there. Without it a pooled fit could only ever be evaluated on its whole panel.


------------------------------------------------------------------------


#### fit()


Fit on a training triangle (already sliced with as_of).


Usage

``` python
fit(triangle, **kwargs)
```


------------------------------------------------------------------------


#### predict()


Predictive distribution of the fitted quantities (e.g. ultimates).


Usage

``` python
predict(segment=None, **kwargs)
```


`segment` names one cohort of this fit (see [cohorts()](gallery.GalleryEntry.md#ibnr.gallery.GalleryEntry.cohorts)), and means the same thing on every entry in the gallery: the returned targets describe that cohort and nothing else. A single-cohort fit accepts None or a matching key and returns the identical object either way; a pooled fit returns that one cohort's targets plus its total, and returns the whole panel (no total - a total across companies is not a quantity) when the segment is omitted.

A segment that names no cohort of this fit raises rather than being ignored: accepting and discarding it would make a typo'd cohort key score the fitted cohort and return a plausible number.


------------------------------------------------------------------------


#### realized_ultimates()


Outcomes aligned, element for element, to `predict(segment)`'s targets.


Usage

``` python
realized_ultimates(full_triangle, segment=None)
```


Read from the FULL (unsliced) triangle, restricted to the origins the training slice actually had - the Schedule P mart carries accident years past the study window and an unrestricted aggregate is silently inflated (measured at 2.4x once, in `scripts/compare_gallery.py`).

On the ABC because it is what makes a cross-model outcome table possible: it was implemented on all 15 entries under a convention nothing enforced, and the convention had already drifted across two families.