faissknn 0.0.2

FAISS implementation of multiclass and multilabel K-Nearest Neighbors Classifiers.

FAISSKNN

faissknn contains implementations for both multiclass and multilabel K-Nearest Neighbors Classifier implementations. The classifiers follow the scikit-learn: fit, predict, and predict_proba methods.

Install

The FAISS authors recommend to install faiss through conda e.g. conda install -c pytorch faiss-gpu. See FAISS install page for more info.

Once faiss is installed, faissknn can be installed through pypi:

pip install faissknn

Usage

Multiclass:

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

from faissknn import FaissKNNClassifier

x, y = make_classification()
x_train, x_test, y_train, y_test = train_test_split(x, y)
model = FaissKNNClassifier(
    n_neighbors=5,
    n_classes=None,
    device="cpu"
)
model.fit(x_train, y_train)

y_pred = model.predict(x_test) # (N,)
y_proba = model.predict_proba(x_test) # (N, C)

Multilabel:

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

from faissknn import FaissKNNMultilabelClassifier

x, y = make_multilabel_classification()
x_train, x_test, y_train, y_test = train_test_split(x, y)
model = FaissKNNMultilabelClassifier(
    n_neighbors=5,
    device="cpu"
)
model.fit(x_train, y_train)

y_pred = model.predict(x_test) # (N, C)
y_proba = model.predict_proba(x_test) # (N, C)

GPU/CUDA: faissknn also supports running on the GPU to speed up computation. Simply change the device to cuda or a specific cuda device cuda:0

model = FaissKNNClassifier(
    n_neighbors=5,
    device="cuda"
)
model = FaissKNNClassifier(
    n_neighbors=5,
    device="cuda:0"
)