reppi 0.1.13

A Python toolkit for representation-based learning/classification algorithms

reppi

A Python library for representation learning — sparse coding and dictionary learning algorithms implemented close to their original formulations.

Installation

pip install reppi

Algorithms

Algorithm	Class	Reference
Orthogonal Matching Pursuit	OMP	Elad et al., 2008
K-SVD	KSVD	Aharon et al., 2006
Label Consistent K-SVD	LCKSVD	Jiang et al., 2011
Frozen Dictionary Learning	IncrementalFrozenDictionary	Carroll et al., 2017

Convention

reppi follows the column-major convention common in the sparse representation literature: signals are columns, so data matrices are shaped (n_features, n_samples). This matches the MATLAB toolboxes the implementations are based on. If your data is in sklearn's (n_samples, n_features) layout, transpose before passing it in.

Quick Start

Installaion

pip install reppi

Sparse Coding with OMP

from reppi import OMP
import numpy as np

# D: (n_features, n_atoms), unit-norm columns
# X: (n_features, n_samples)
omp = OMP(n_nonzero_coefs=10)
Gamma = omp.encode(X, D)  # (n_atoms, n_samples)

Dictionary Learning with K-SVD

from reppi import KSVD

ksvd = KSVD(
    n_components=128,     # number of atoms
    n_nonzero_coefs=10,   # sparsity level T
    n_iter=20,
    verbose=True,
)
ksvd.fit(X_train)         # X_train: (n_features, n_samples)

D = ksvd.D_               # learned dictionary (n_features, n_components)
Gamma = ksvd.transform(X) # sparse codes (n_components, n_samples)

Discriminative Dictionary Learning with LC-KSVD

LC-KSVD jointly learns a dictionary and a linear classifier from labelled data. Labels are passed as a one-hot matrix H of shape (n_classes, n_samples).

LC-KSVD1 — reconstruction + label-consistency:

from reppi import LCKSVD

model = LCKSVD(
    n_components=570,
    n_nonzero_coefs=30,
    alpha=4.0,            # weight for label-consistency term
    variant="lcksvd1",
    n_iter=50,
    n_iter_init=20,       # K-SVD iterations for initialisation
    verbose=True,
)
model.fit(X_train, H_train)
Gamma = model.transform(X_test)  # (n_components, n_samples)

LC-KSVD2 — reconstruction + label-consistency + classification error:

model = LCKSVD(
    n_components=570,
    n_nonzero_coefs=30,
    alpha=4.0,
    beta=2.0,             # weight for classifier term
    variant="lcksvd2",
    n_iter=50,
    n_iter_init=20,
    verbose=True,
)
model.fit(X_train, H_train)

predictions = model.predict(X_test)        # integer class indices
accuracy    = model.score(X_test, H_test)  # float in [0, 1]

Frozen Dictionary Learning

reppi also supports incremental frozen dictionary learning for scenarios where a base dictionary is learned first and then extended over time with new class-specific residual dictionaries.

The learned dictionary grows as:

D = [ D_n | D_a_1 | D_a_2 | ... | D_a_k ]

Where:

D_n is the frozen base/background dictionary D_a_i is the residual dictionary learned for class i

Previously learned atoms remain frozen when new classes are added.

Single Frozen Residual Step

Use FrozenDictionaryLearner for a single residual-learning stage:

from reppi.dictionary.frozen import FrozenDictionaryLearner
from reppi import LCKSVD

frozen = FrozenDictionaryLearner(
    D_frozen=D_base,
    learner_class=LCKSVD,
    learner_kwargs=dict(
        n_components=32,
        n_nonzero_coefs=10,
        variant="lcksvd2",
    ),
    n_nonzero_coefs=10,
)

frozen.fit(X_class, H_class)

D_combined = frozen.D_combined_
Gamma = frozen.transform(X_test)
predictions = frozen.predict(X_test)

Incremental Frozen Dictionary Pipeline

Use IncrementalFrozenDictionary for full sequential learning:

from reppi.dictionary.frozen import IncrementalFrozenDictionary
from reppi import LCKSVD

inc = IncrementalFrozenDictionary(
    base_learner_class=LCKSVD,
    base_learner_kwargs=dict(
        n_components=128,
        n_nonzero_coefs=10,
        variant="lcksvd2",
    ),
    residual_learner_class=LCKSVD,
    residual_learner_kwargs=dict(
        n_components=32,
        n_nonzero_coefs=10,
        variant="lcksvd2",
    ),
    n_nonzero_coefs=10,
)

References

• M. Aharon, M. Elad, A. Bruckstein. "The K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation". IEEE Trans. Signal Processing, 54(11), 2006.
• M. Elad, R. Rubinstein, M. Zibulevsky. "Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit". Technion Technical Report, 2008.
• Z. Jiang, Z. Lin, L. Davis. "Learning A Discriminative Dictionary for Sparse Coding via Label Consistent K-SVD". CVPR, 2011.
• B. T. Carroll, B. M. Whitaker, W. Daley, D. V. Anderson. "Outlier Learning via Augmented Frozen Dictionaries". IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2017.