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Version: 1.1.0

Detect data drift with the k-core distance

We compute the cosine distance of the k-nearest neighbor in the embedding space for each data sample. This distance can be used to detect outliers/drifted samples. We find a suitable outlier threshold by inspecting the data with Spotlight.

Use Chrome to run Spotlight in Colab. Due to Colab restrictions (e.g. no websocket support), the performance is limited. Run the notebook locally for the full Spotlight experience.

Open In Colab
  • df['embedding'] contain the embeddings for each data sample


Imports and play as copy-n-paste functions

# Install dependencies
#@title Install required packages with PIP

!pip install renumics-spotlight datasets
# Play as copy-n-paste functions
#@title Play as copy-n-paste functions

from sklearn.neighbors import NearestNeighbors
import pandas as pd
import numpy as np
import datasets
from renumics import spotlight

def compute_k_core_distances(df, k=8, embedding_name='embedding'):
features = np.stack(df[embedding_name].to_numpy())
neigh = NearestNeighbors(n_neighbors=k, metric='cosine')
distances, indices = neigh.kneighbors()

df_out['k_core_index']=indices[:, -1]

return df_out

Step-by-step example on CIFAR-100

Load CIFAR-100 from Huggingface hub and convert it to Pandas dataframe

dataset = datasets.load_dataset("renumics/cifar100-enriched", split="train")
df = dataset.to_pandas()

Compute k-nearest neighbor distances

df_kcore = compute_k_core_distances(df)
df = pd.concat([df, df_kcore], axis=1)

Inspect candidates for data drift with Spotlight

df_show = df.drop(columns=['embedding', 'probabilities'])
layout_url = ""
response = requests.get(layout_url)
layout = spotlight.layout.nodes.Layout(**json.loads(response.text)), dtype={"image": spotlight.Image, "embedding_reduced": spotlight.Embedding}, layout=layout)