Find outliers with Cleanlab
We use the Cleanlab library to compute outlier scores. We then manually inspect the data points to correct them.
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.
- inputs
- outputs
- parameters
df['probabilities']
contain the class probability vector that was inferred by the modeldf['embedding']
contain the embeddings for each data sample
df_leak['outlier_score_embedding']
contains an outlier score [0,1] that was computed based on the embedding of each data sample.df_leak['outlier_score_prediction']
contains an outlier score [0,1] that was computed based on the inferred probabilities of each data samples.
Imports and play as copy-n-paste functions
# Install dependencies
#@title Install required packages with PIP
!pip install renumics-spotlight cleanlab datasets
# Play as copy-n-paste functions
#@title Play as copy-n-paste functions
import datasets
from renumics import spotlight
from cleanlab.outlier import OutOfDistribution
import numpy as np
import pandas as pd
import requests
def outlier_score_cleanlab(df, embedding_name='embedding', probabilities_name='probabilities', label_name='labels'):
embs = np.stack(df[embedding_name].to_numpy())
probs = np.stack(df[probabilities_name].to_numpy())
labels = df[label_name].to_numpy()
ood = OutOfDistribution()
ood_train_feature_scores = ood.fit_score(features=np.stack(embs))
ood_train_predictions_scores = ood.fit_score(pred_probs=probs, labels=labels)
df_out=pd.DataFrame()
df_out['outlier_score_embedding']=ood_train_feature_scores
df_out['outlier_score_prediction']=ood_train_predictions_scores
return df_out
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 outlier scores with Cleanlab
df_outliers = outlier_score_cleanlab(df, label_name='fine_label')
df = pd.concat([df, df_outliers], axis=1)
Inspect outliers and remove them with Spotlight
df_show = df.drop(columns=['embedding', 'probabilities'])
layout_url = "https://raw.githubusercontent.com/Renumics/spotlight/playbook_initial_draft/playbook/rookie/outlier_cleanlab.json"
response = requests.get(layout_url)
layout = spotlight.layout.nodes.Layout(**json.loads(response.text))
spotlight.show(df_show, dtype={"image": spotlight.Image, "embedding_reduced": spotlight.Embedding}, layout=layout)