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feat: allow choosing reduction type on animation #44

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Mar 13, 2024
Merged

feat: allow choosing reduction type on animation #44

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7 changes: 5 additions & 2 deletions clip_eval/cli/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,15 +106,18 @@ def evaluate_embeddings(
The interface will prompt you to choose which embeddings you want to use.
""",
)
def animate_embeddings(interactive: Annotated[bool, Option(help="Interactive plot instead of animation")] = False):
def animate_embeddings(
interactive: Annotated[bool, Option(help="Interactive plot instead of animation")] = False,
reduction: Annotated[str, Option(help="Reduction type [pca, tsne, umap (default)")] = "umap",
):
from clip_eval.plotting.animation import build_animation, save_animation_to_file

# Error could be localised better
defns = select_existing_embedding_definitions(by_dataset=True)
assert len(defns) == 2, "Please select exactly two models to make animation"
def1 = max(defns, key=lambda d: int(d.model == "clip"))
def2 = defns[0] if defns[0] != def1 else defns[1]
res = build_animation(def1, def2, interactive=interactive)
res = build_animation(def1, def2, interactive=interactive, reduction=reduction)

if res is None:
plt.show()
Expand Down
10 changes: 6 additions & 4 deletions clip_eval/plotting/animation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,8 +166,8 @@ def rotate_to_target(source: N2Array, destination: N2Array) -> N2Array:
source = np.pad(source, [(0, 0), (0, 1)], mode="constant", constant_values=0.0)
destination = np.pad(destination, [(0, 0), (0, 1)], mode="constant", constant_values=0.0)

rot, *_ = R.align_vectors(source, destination, return_sensitivity=True)
out = source @ rot.as_matrix()
rot, *_ = R.align_vectors(destination, source, return_sensitivity=True)
out = rot.apply(source)
return out[:, :2]


Expand Down Expand Up @@ -220,16 +220,18 @@ def build_animation(
reducer = reduction_from_string(reduction)
reduced_1 = standardize(reducer.get_reduction(defn_1))
reduced_2 = rotate_to_target(standardize(reducer.get_reduction(defn_2)), reduced_1)
labels = embeds.labels

if reduced_1.shape[0] > 2_000:
selection = np.random.permutation(reduced_1.shape[0])[2_000]
selection = np.random.permutation(reduced_1.shape[0])[:2_000]
reduced_1 = reduced_1[selection]
reduced_2 = reduced_2[selection]
labels = labels[selection]

return create_embedding_chart(
reduced_1,
reduced_2,
embeds.labels,
labels,
defn_1.model,
defn_2.model,
suptitle=dataset.title,
Expand Down
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