Source code for librosa.util._nnls

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Non-negative least squares"""

# The scipy library provides an nnls solver, but it does
# not generalize efficiently to matrix-valued problems.
# We therefore provide an alternate solver here.
# The vectorized solver uses the L-BFGS-B over blocks of
# data to efficiently solve the constrained least-squares problem.

import numpy as np
import scipy.optimize
from .utils import MAX_MEM_BLOCK

__all__ = ["nnls"]

def _nnls_obj(x, shape, A, B):
    """Compute the objective and gradient for NNLS"""

    # Scipy's lbfgs flattens all arrays, so we first reshape
    # the iterate x
    x = x.reshape(shape)

    # Compute the difference matrix
    diff =, x) - B

    # Compute the objective value
    value = 0.5 * np.sum(diff ** 2)

    # And the gradient
    grad =, diff)

    # Flatten the gradient
    return value, grad.flatten()

def _nnls_lbfgs_block(A, B, x_init=None, **kwargs):
    """Solve the constrained problem over a single block

    A : np.ndarray [shape=(m, d)]
        The basis matrix

    B : np.ndarray [shape=(m, N)]
        The regression targets

    x_init : np.ndarray [shape=(d, N)]
        An initial guess

        Additional keyword arguments to `scipy.optimize.fmin_l_bfgs_b`

    x : np.ndarray [shape=(d, N)]
        Non-negative matrix such that Ax ~= B

    # If we don't have an initial point, start at the projected
    # least squares solution
    if x_init is None:
        x_init = np.linalg.lstsq(A, B, rcond=None)[0]
        np.clip(x_init, 0, None, out=x_init)

    # Adapt the hessian approximation to the dimension of the problem
    kwargs.setdefault("m", A.shape[1])

    # Construct non-negative bounds
    bounds = [(0, None)] * x_init.size
    shape = x_init.shape

    # optimize
    x, obj_value, diagnostics = scipy.optimize.fmin_l_bfgs_b(
        _nnls_obj, x_init, args=(shape, A, B), bounds=bounds, **kwargs
    # reshape the solution
    return x.reshape(shape)

[docs]def nnls(A, B, **kwargs): """Non-negative least squares. Given two matrices A and B, find a non-negative matrix X that minimizes the sum squared error:: err(X) = sum_i,j ((AX)[i,j] - B[i, j])^2 Parameters ---------- A : np.ndarray [shape=(m, n)] The basis matrix B : np.ndarray [shape=(m, N)] The target matrix. kwargs Additional keyword arguments to `scipy.optimize.fmin_l_bfgs_b` Returns ------- X : np.ndarray [shape=(n, N), non-negative] A minimizing solution to ``|AX - B|^2`` See Also -------- scipy.optimize.nnls scipy.optimize.fmin_l_bfgs_b Examples -------- Approximate a magnitude spectrum from its mel spectrogram >>> y, sr = librosa.load(librosa.ex('trumpet'), duration=3) >>> S = np.abs(librosa.stft(y, n_fft=2048)) >>> M = librosa.feature.melspectrogram(S=S, sr=sr, power=1) >>> mel_basis = librosa.filters.mel(sr, n_fft=2048, n_mels=M.shape[0]) >>> S_recover = librosa.util.nnls(mel_basis, M) Plot the results >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True) >>> librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max), ... y_axis='log', x_axis='time', ax=ax[2]) >>> ax[2].set(title='Original spectrogram (1025 bins)') >>> ax[2].label_outer() >>> librosa.display.specshow(librosa.amplitude_to_db(M, ref=np.max), ... y_axis='mel', x_axis='time', ax=ax[0]) >>> ax[0].set(title='Mel spectrogram (128 bins)') >>> ax[0].label_outer() >>> img = librosa.display.specshow(librosa.amplitude_to_db(S_recover, ref=np.max(S)), ... y_axis='log', x_axis='time', ax=ax[1]) >>> ax[1].set(title='Reconstructed spectrogram (1025 bins)') >>> ax[1].label_outer() >>> fig.colorbar(img, ax=ax, format="%+2.0f dB") """ # If B is a single vector, punt up to the scipy method if B.ndim == 1: return scipy.optimize.nnls(A, B)[0] n_columns = MAX_MEM_BLOCK // (A.shape[-1] * A.itemsize) n_columns = max(n_columns, 1) # Process in blocks: if B.shape[-1] <= n_columns: return _nnls_lbfgs_block(A, B, **kwargs).astype(A.dtype) x = np.linalg.lstsq(A, B, rcond=None)[0].astype(A.dtype) np.clip(x, 0, None, out=x) x_init = x for bl_s in range(0, x.shape[-1], n_columns): bl_t = min(bl_s + n_columns, B.shape[-1]) x[:, bl_s:bl_t] = _nnls_lbfgs_block( A, B[:, bl_s:bl_t], x_init=x_init[:, bl_s:bl_t], **kwargs ) return x