Add a ConceptLocalizer via Black-box Attributions

[Agustin-Picard]

This PR introduces input-to-concept localization for HolisticCraft,
answering the question "which regions of the input are responsible for
activating concept k?" by exposing the fitted concept extractor as a
K-output black-box model and delegating to any compatible Xplique
attribution method (initially validated with Rise and
SobolAttributionMethod).

It complements — but does not replace — the existing coefficient-based
concept visualization, and reuses the standard Xplique attribution
machinery instead of introducing a parallel concept-specific explanation
stack.

Motivation

HolisticCraft currently visualizes concept coefficients U_k(x) as spatial
heatmaps by resizing them to the input resolution. This is a direct
visualization of the latent coefficients — not an input attribution — and can
be misleading for deep architectures, large receptive fields, attention-based
models, complex latent cuts, multi-scale models, and object-detection
backbones.

This PR adds a second, complementary interpretation:

flowchart LR
    X["input x"] --> LOC["ConceptLocalizer"]
    subgraph LOC_INTERNALS ["ConceptLocalizer internals"]
        direction LR
        E["latent extractor"] --> F["factorizer.encode(...)"]
        F --> R["spatial reduction<br/>per concept"]
        R --> S["[s_1(x), ..., s_K(x)]"]
    end
    LOC --> LOC_INTERNALS
    LOC_INTERNALS --> BB["black-box attribution<br/>method targeting concept k"]
    BB --> H["input-space<br/>localization heatmap"]

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The ConceptLocalizer behaves like a K-output black-box model, so
concept k can be targeted with an ordinary one-hot vector, without any
custom attribution operator.

The proposed abstraction is symmetric with the existing ConceptDecoder:

flowchart LR
    subgraph EXISTING ["Existing"]
        direction LR
        C1["concept coefficients"] --> D1["ConceptDecoder"] --> P1["final prediction"]
    end
    subgraph NEW ["New"]
        direction LR
        I2["input image"] --> L2["ConceptLocalizer"] --> S2["concept scores"]
    end

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Concept vocabulary

The PR makes the three distinct quantities explicit throughout docstrings:

Concept	Answers	Existing / New
Concept activation	How strongly concept k is present in the latent	Existing
Concept importance	How much concept k contributes to the task prediction	Existing
Concept localization	Which input regions drive activation of concept k	New

What this PR adds

New abstractions

• ConceptLocalizer (xplique/concepts/holistic_craft.py)

  • Framework-agnostic base that maps encoded concept coefficients to one
    scalar score per concept.
  • Supports string reducers "mean", "sum", "max" and arbitrary
    callables. Default is "mean" (consistent with the existing
    top-image ranking).
  • Handles spatial [B, H, W, K], token [B, T, K], and already-global
    [B, K] coefficient layouts; validates output shape and finiteness.
  • Wraps NotImplementedError from non-inductive factorizers into a
    clear RuntimeError explaining that factorizer.encode() is
    required because attribution methods evaluate perturbed inputs.
  • Signed coefficients are preserved; users can pass a callable reducer
    (e.g. mean absolute activation) when magnitude is intended.

• ConceptLocalizerTf (xplique/concepts/tf/holistic_craft.py)

  • Callable that returns a tf.Tensor with shape (B, K).

• ConceptLocalizerTorch (xplique/concepts/torch/holistic_craft.py)

  • nn.Module that accepts native NCHW tensors.
  • HolisticCraftTorch.make_concept_localizer() returns it wrapped in
    Xplique's existing TorchWrapper with is_channel_first=True and
    requires_grad=False, so Xplique's standard NHWC-to-NCHW conversion
    and device handling apply and no gradient path is used.

New HolisticCraft API

• HolisticCraft.make_concept_localizer(concept_reducer="mean") (abstract,
  implemented per framework). Mirrors the existing
  make_concept_decoder(latent_data) symmetry:

  ConceptDecoder   : coefficients -> prediction
  ConceptLocalizer : input        -> concept scores
  

• HolisticCraft.compute_concept_attributions(images, partial_explainer, ...)

  • Accepts any PartialExplainer configured with a compatible
    black-box explainer (validated for Rise and
    SobolAttributionMethod; compatible with Occlusion, Lime,
    KernelShap, and callable explainer factories).
  • Rejects WhiteBoxExplainer subclasses and any explicit operator
    argument with an actionable error.
  • Validates concept_ids (integers, no booleans, no duplicates,
    in range, non-empty) before any expensive work; enforces
    non-empty batches with positive spatial dimensions.
  • Normalizes image batches to Xplique's channel-last convention
    (NHWC), including PyTorch Tensor / list inputs.
  • Builds the localizer and the explainer instance once and reuses
    them across all requested concepts.
  • Returns float32 maps of shape (N, H, W, number_of_concepts).
    Uncomputed channels are NaN so that "not computed" is
    unambiguously distinguishable from a valid zero attribution.
    concept_ids=None returns a fully finite tensor.
  • Accepts explainer outputs shaped (N, H, W) or (N, H, W, 1);
    unexpected shapes / channel counts / non-finite values are rejected
    with informative errors.

Visualization integration

• display_concept_heatmap()
  • Extended to also accept input-resolution attribution maps.
  • Skips the resize step when the heatmap already matches the image
    resolution (avoids needlessly resampling RISE / Sobol maps).
  • Squeezes (H, W, 1) maps to (H, W) and validates finiteness.
• display_images_per_concept(..., concept_maps=None)
  • When concept_maps=None, behavior is identical to v2.0.0 (uses
    coeffs_u).
  • When concept_maps is provided, it drives the overlays; coeffs_u
    keeps its original meaning.
• display_top_images_per_concept(..., concept_maps=None)
  • Top-image ranking remains coefficient-based (coeffs_u);
    attribution maps only drive the overlays. This preserves the
    semantic that concept-coefficient magnitude represents presence
    while attribution maps represent input sensitivity of the concept
    score.
• Shared helpers _normalize_image_batch_to_nhwc,
  _validate_concept_ids, _prepare_concept_maps, and
  _resolve_concept_map_source back both display paths and the new
  attribution API, eliminating the previous duplicated NHWC / order
  validation and concentrating localization-specific logic in one
  place.

Backward compatibility

• All existing calls to display_images_per_concept(images[, coeffs_u=...])
  and display_top_images_per_concept(images[, coeffs_u=...]) retain the
  exact v2.0.0 rendering and ordering behavior.
• No existing user incurs additional attribution computation.
• ConceptDecoder, compute_explanation_per_concept(),
  estimate_importance(), and get_topk_images_per_concept() are
  not modified.
• Public API additions are opt-in.

Documentation and tutorials

Colab tutorial (simple for now): https://colab.research.google.com/drive/19SRZXMVztdSPEUJm5ggT-hoTujVzybnV?usp=sharing