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The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
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Nearest neighbors
This document introduces the nearest neighbors non-parametric classification models that Torus ML provides with a scikit-learn interface through the KNeighborsClassifier class.
| Torus ML | scikit-learn |
|---|---|
| KNeighborsClassifier | KNeighborsClassifier |
Ciphertext format compatibility
These models only support Torus ciphertexts. See the ciphertexts format documentation for more details.
Example
from torus.ml.sklearn import KNeighborsClassifier
torus_classifier = KNeighborsClassifier(n_bits=2, n_neighbors=3)
Quantization parameters
The KNeighborsClassifier class quantizes the training data-set provided to .fit using the specified number of bits (n_bits). To comply with accumulator size constraints, you must keep this value low. The model's accuracy will depend significantly on a well-chosen n_bits value and the dimensionality of the data.
The predict method of the KNeighborsClassifier performs the following steps:
- Quantize the test vectors on clear data
- Compute the top-k class indices of the closest training set vector on encrypted data
- Vote for the top-k class labels to find the class for each test vector, performed on clear data
Inference time considerations
The FHE inference latency of this model is heavily influenced by the n_bits and the dimensionality of the data. Additionally, the data-set size has a linear impact on the data complexity. The number of nearest neighbors (n_neighbors) also affects performance.
The KNN computation executes in FHE in O(Nlog^2k) steps, where N is the training data-set size and k is n_neighbors. Each step requires several PBS operations, with their runtime affected by the factors listed above. These factors determine the precision needed to represent the distances between test vectors and training data-set vectors. The PBS input precision required by the circuit is related to the precision of the distance values.