AiNet

Artificial Immune Network (AiNet) for Compression and Clustering.

Inheritance

This class extends BaseClusterer.

Module: aisp.ina
Import: from aisp.ina import AiNet

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Overview

This class implements the aiNet algorithm, an artificial immune network model designed for clustering and data compression tasks. The aiNet algorithm uses principles from immune network theory, clonal selection, and affinity maturation to compress high-dimensional datasets ¹.
For clustering, the class uses SciPy implementation of the Minimum Spanning Tree (MST) to remove the most distant nodes and separate the groups ².

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Example

import numpy as np
from aisp.ina import AiNet

np.random.seed(1)
# Generating training data
a = np.random.uniform(high=0.4, size=(50, 2))
b = np.random.uniform(low=0.6, size=(50, 2))
x_train = np.vstack((a, b))
# AiNet Instance
ai_net = AiNet(
    N=150,
    mst_inconsistency_factor=1,
    seed=1,
    affinity_threshold=0.85,
    suppression_threshold=0.7
)
ai_net = ai_net.fit(x_train, verbose=True)
x_test = [
    [0.15, 0.45],  # Expected: label 0
    [0.85, 0.65],  # Expected: label 1
]
y_pred = ai_net.predict(x_test)
print(y_pred)

Output

[0 1]

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Constructor Parameters

Name	Type	Default	Description
N	int	50	Number of memory cells (antibodies) in the population.
n_clone	int	10	Number of clones generated for each selected memory cell.
top_clonal_memory_size	int	5	Number of highest-affinity antibodies selected per antigen for cloning and mutation.
n_diversity_injection	int	5	Number of new random memory cells injected to maintain diversity.
affinity_threshold	float	0.5	Threshold for affinity (similarity) to determine cell suppression or selection.
suppression_threshold	float	0.5	Threshold for suppressing similar memory cells.
mst_inconsistency_factor	float	2.0	Factor used to determine which edges in the Minimum Spanning Tree (MST) are considered inconsistent.
max_iterations	int	10	Maximum number of training iterations.
k	int	3	The number of K nearest neighbors that will be used to choose a label in the prediction.
metric	MetricType	"euclidean"	Distance metric used to compute similarity between memory cells.
seed	Optional[int]	None	Seed for random generation.
use_mst_clustering	bool	True	If True, performs clustering using the MST. If False, clustering is not performed and the predict method raises a ModelNotFittedError.
p	float	2.0	This parameter stores the value of p used in the Minkowski distance.

Attributes

Name	Type	Default	Description
memory_network	Dict[int, List[Cell]]	-	The immune network representing clusters.
population_antibodies	Optional[npt.NDArray]	-	The set of memory antibodies.
mst	dict	-	The Minimum Spanning Tree and its statistics (graph, mean_distance, std_distance).

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Public Methods

fit

def fit(self, X: Union[npt.NDArray, list], verbose: bool = True) -> AiNet:
    ...

Train the AiNet model on input data.

Parameters

Name	Type	Default	Description
X	Union[npt.NDArray, list]	-	Training input samples. Each row corresponds to a samples and column to feature.
verbose	bool	True	If True, prints training progress information.

Returns

Type	Description
Self	Returns the instance itself.

Raises

Exception	Description
TypeError	If X is not a ndarray or list.
UnsupportedTypeError	If the data type of the feature on X is not supported.

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predict

def predict(self, X: Union[npt.NDArray, list]) -> npt.NDArray:
    ...

Predict cluster labels for input data.

Parameters

Name	Type	Default	Description
X	Union[npt.NDArray, list]	-	Input samples. Must have the same number of features used during training.

Returns

Type	Description
npt.NDArray	Predicted cluster labels, or None if clustering is disabled.

Raises

Exception	Description
TypeError	If X is not a ndarray or list.
ValueError	If X contains values other than (0 and 1) or (True and False) when the trained features are of type "binary-features".
FeatureDimensionMismatch	If the number of features (columns) in X does not match the expected number.
ModelNotFittedError	If the mode has not yet been adjusted and does not have defined memory cells, it is not able to predictions.

---

update_clusters

def update_clusters(self, mst_inconsistency_factor: Optional[float] = None):
    ...

Partition the clusters based on the MST inconsistency factor.

Uses the precomputed Minimum Spanning Tree (MST) of the antibody population to redefine clusters. Edges whose weights exceed the mean plus the mst_inconsistency_factor multiplied by the standard deviation of MST edge weights are removed. Each connected component after pruning is treated as a distinct cluster.

Parameters

Name	Type	Default	Description
mst_inconsistency_factor	float	None	If provided, overrides the current inconsistency factor.

Raises

Exception	Description
ValueError	If the Minimum Spanning Tree (MST) has not yet been created.
ValueError	If Population of antibodies is empty.
ValueError	If MST statistics (mean or std) are not available.

Updates

Name	Type	Description
memory_network	dict[int, npt.NDArray]	Dictionary mapping cluster labels to antibody arrays.
labels	list	List of cluster labels.

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Extended Example

Complete usage examples are available in the Jupyter Notebooks:

• Examples

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References

Footnotes

1. De Castro, Leandro & José, Fernando & von Zuben, Antonio Augusto. (2001). aiNet: An Artificial Immune Network for Data Analysis. Available at: https://www.researchgate.net/publication/228378350_aiNet_An_Artificial_Immune_Network_for_Data_Analysis ↩

2. SciPy Documentation. Minimum Spanning Tree. https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csgraph.minimum_spanning_tree ↩