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Revised docstrings for some functions in run_eval.py #2

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Revised docstrings for some functions in run_eval.py #2

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8 changes: 2 additions & 6 deletions experiments/run_eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -201,15 +201,13 @@ def faster_svm_eval(X, y, n_splits=10, **kwargs):
def random_triplet_eval(X, X_new, num_triplets=5):
'''
This is a function that is used to evaluate the lower dimension embedding.
An triplet satisfaction score is calculated by evaluating how many randomly
A triplet satisfaction score is calculated by evaluating how many randomly
selected triplets have been violated. Each point will generate 5 triplets.
Input:
X: A numpy array with the shape [N, p]. The higher dimension embedding
of some dataset. Expected to have some clusters.
X_new: A numpy array with the shape [N, k]. The lower dimension embedding
of some dataset. Expected to have some clusters as well.
y: A numpy array with the shape [N, 1]. The labels of the original
dataset. Used to identify clusters
Output:
acc: The score generated by the algorithm.
'''
Expand Down Expand Up @@ -249,8 +247,6 @@ def neighbor_kept_ratio_eval(X, X_new, n_neighbors=30):
of some dataset. Expected to have some clusters.
X_new: A numpy array with the shape [N, k]. The lower dimension embedding
of some dataset. Expected to have some clusters as well.
y: A numpy array with the shape [N, 1]. The labels of the original
dataset. Used to identify clusters
Output:
acc: The score generated by the algorithm.

Expand Down Expand Up @@ -468,7 +464,7 @@ def spearman_correlation_eval(X, X_new, n_points=1000, random_seed=100):
'''Evaluate the global structure of an embedding via spearman correlation in
distance matrix, following https://www.nature.com/articles/s41467-019-13056-x
'''
# Fix the random seed to ensure reproducability
# Fix the random seed to ensure reproducibility
rng = np.random.default_rng(seed=random_seed)
dataset_size = X.shape[0]

Expand Down