Example: Approximate hubness reduction

This example shows how to combine approximate nearest neighbor search and hubness reduction in order to perform approximate hubness reduction for large data sets.

from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

from skhubness.analysis import Hubness
from skhubness.neighbors import KNeighborsClassifier

# High-dimensional artificial data
X, y = make_classification(n_samples=1_000_000,
                           n_features=500,
                           n_informative=400,
                           random_state=543)

X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    test_size=10_000,
                                                    stratify=y,
                                                    shuffle=True,
                                                    random_state=2346)

# Approximate hubness estimation
hub = Hubness(k=10,
              return_value='robinhood',
              algorithm='hnsw',
              random_state=2345,
              shuffle_equal=False,
              n_jobs=-1,
              verbose=2)
hub.fit(X_train)
robin_hood = hub.score(X_test)
print(f'Hubness (Robin Hood): {robin_hood:.3f}')
# 0.944

# Approximate hubness reduction for classification
knn = KNeighborsClassifier(n_neighbor=10,
                           algorithm='hnsw',
                           hubness='ls',
                           n_jobs=-1,
                           verbose=2)

knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f'Test accuracy: {acc:.3f}')
# Test accuracy: 0.987