anon-double-blind · April 24, 2020 00:38
diff --git a/confidence.py b/confidence.py
 from sklearn.metrics import silhouette_samples
 import numpy as np
 from sklearn.mixture import GaussianMixture
 from sklearn.cluster import KMeans
 from scipy.special import logsumexp

 def softmax(x, axis=None):
    return np.exp(x - logsumexp(x, axis=axis, keepdims=True))

 def _get_loud_bins_mask(threshold, stft):
    representation = np.abs(stft)
    threshold = np.percentile(representation, threshold)
    mask = representation > threshold
    return mask, representation

 def posterior_confidence(stft, features, num_sources, threshold=95, 
                         **kwargs):
    """
    Calculates the clusterability of an embedding space by looking at the
    strength of the assignments of each point to a specific cluster. The 
    more points that are "in between" clusters (e.g. no strong assignmment),
    the lower the clusterability.

    Args:
        stft (np.ndarray): STFT array which will be used to compute
          the mask over which to compute the confidence measure.
        features (np.ndarray): Numpy array containing the features to be clustered. 
          Should have the same dimensions as the representation.
        n_sources (int): Number of sources to cluster the features into.
        threshold (int, optional): Threshold by loudness. Points below the threshold are
          excluded from being used in the confidence measure. Defaults to 95.
        kwargs: Keyword arguments to `_get_loud_bins_mask`. Namely, representation can
          go here as a keyword argument.
    
    Returns:
        float: Confidence given by posteriors.
    """
    mask, _ = _get_loud_bins_mask(threshold, stft, **kwargs)
    embedding_size = features.shape[-1]
    features = features[mask].reshape(-1, embedding_size)

    kmeans = KMeans(num_sources)
    distances = kmeans.fit_transform(features)

    confidence = softmax(-distances, axis=-1)

    confidence = (
        (num_sources * np.max(confidence, axis=-1) - 1) / 
        (num_sources - 1)
    )

    return confidence.mean()

 def silhouette_confidence(stft, features, num_sources, threshold=95, 
                          max_points=1000, **kwargs):
    """
    Uses the silhouette score to compute the clusterability of the feature space.

    The Silhouette Coefficient is calculated using the 
    mean intra-cluster distance (a) and the mean nearest-cluster distance (b) 
    for each sample. The Silhouette Coefficient for a sample is (b - a) / max(a, b). 
    To clarify, b is the distance between a sample and the nearest cluster 
    that the sample is not a part of. Note that Silhouette Coefficient is 
    only defined if number of labels is 2 <= n_labels <= n_samples - 1.

    References:

    Peter J. Rousseeuw (1987). “Silhouettes: a Graphical Aid to the 
    Interpretation and Validation of Cluster Analysis”. Computational and 
    Applied Mathematics 20: 53-65.
    
    Args:
        stft (np.ndarray): STFT array which will be used to compute
          the mask over which to compute the confidence measure.
        features (np.ndarray): Numpy array containing the features to be clustered. 
          Should have the same dimensions as the representation.
        n_sources (int): Number of sources to cluster the features into.
        threshold (int, optional): Threshold by loudness. Points below the threshold are
          excluded from being used in the confidence measure. Defaults to 95.
        kwargs: Keyword arguments to `_get_loud_bins_mask`. Namely, representation can
          go here as a keyword argument.
        max_points (int, optional): Maximum number of points to compute the Silhouette
          score for. Silhouette score is a costly operation. Defaults to 1000.
    
    Returns:
        float: Confidence given by Silhouette score.
    """
    mask, _ = _get_loud_bins_mask(threshold, stft, **kwargs)
    embedding_size = features.shape[-1]
    features = features[mask].reshape(-1, embedding_size)

    if features.shape[0] > max_points:
        idx = np.random.choice(
            np.arange(features.shape[0]), max_points,
            replace=False)
        features = features[idx]
    
    kmeans = KMeans(num_sources)

    labels = kmeans.fit_predict(features)
    confidence = silhouette_samples(features, labels)

    return confidence.mean()
	from sklearn.metrics import silhouette_samples
	import numpy as np
	from sklearn.mixture import GaussianMixture
	from sklearn.cluster import KMeans
	from scipy.special import logsumexp

	def softmax(x, axis=None):
	return np.exp(x - logsumexp(x, axis=axis, keepdims=True))

	def _get_loud_bins_mask(threshold, stft):
	representation = np.abs(stft)
	threshold = np.percentile(representation, threshold)
	mask = representation > threshold
	return mask, representation

	def posterior_confidence(stft, features, num_sources, threshold=95,
	**kwargs):
	"""
	Calculates the clusterability of an embedding space by looking at the
	strength of the assignments of each point to a specific cluster. The
	more points that are "in between" clusters (e.g. no strong assignmment),
	the lower the clusterability.

	Args:
	stft (np.ndarray): STFT array which will be used to compute
	the mask over which to compute the confidence measure.
	features (np.ndarray): Numpy array containing the features to be clustered.
	Should have the same dimensions as the representation.
	n_sources (int): Number of sources to cluster the features into.
	threshold (int, optional): Threshold by loudness. Points below the threshold are
	excluded from being used in the confidence measure. Defaults to 95.
	kwargs: Keyword arguments to `_get_loud_bins_mask`. Namely, representation can
	go here as a keyword argument.

	Returns:
	float: Confidence given by posteriors.
	"""
	mask, _ = _get_loud_bins_mask(threshold, stft, **kwargs)
	embedding_size = features.shape[-1]
	features = features[mask].reshape(-1, embedding_size)

	kmeans = KMeans(num_sources)
	distances = kmeans.fit_transform(features)

	confidence = softmax(-distances, axis=-1)

	confidence = (
	(num_sources * np.max(confidence, axis=-1) - 1) /
	(num_sources - 1)
	)

	return confidence.mean()

	def silhouette_confidence(stft, features, num_sources, threshold=95,
	max_points=1000, **kwargs):
	"""
	Uses the silhouette score to compute the clusterability of the feature space.

	The Silhouette Coefficient is calculated using the
	mean intra-cluster distance (a) and the mean nearest-cluster distance (b)
	for each sample. The Silhouette Coefficient for a sample is (b - a) / max(a, b).
	To clarify, b is the distance between a sample and the nearest cluster
	that the sample is not a part of. Note that Silhouette Coefficient is
	only defined if number of labels is 2 <= n_labels <= n_samples - 1.

	References:

	Peter J. Rousseeuw (1987). “Silhouettes: a Graphical Aid to the
	Interpretation and Validation of Cluster Analysis”. Computational and
	Applied Mathematics 20: 53-65.

	Args:
	stft (np.ndarray): STFT array which will be used to compute
	the mask over which to compute the confidence measure.
	features (np.ndarray): Numpy array containing the features to be clustered.
	Should have the same dimensions as the representation.
	n_sources (int): Number of sources to cluster the features into.
	threshold (int, optional): Threshold by loudness. Points below the threshold are
	excluded from being used in the confidence measure. Defaults to 95.
	kwargs: Keyword arguments to `_get_loud_bins_mask`. Namely, representation can
	go here as a keyword argument.
	max_points (int, optional): Maximum number of points to compute the Silhouette
	score for. Silhouette score is a costly operation. Defaults to 1000.

	Returns:
	float: Confidence given by Silhouette score.
	"""
	mask, _ = _get_loud_bins_mask(threshold, stft, **kwargs)
	embedding_size = features.shape[-1]
	features = features[mask].reshape(-1, embedding_size)

	if features.shape[0] > max_points:
	idx = np.random.choice(
	np.arange(features.shape[0]), max_points,
	replace=False)
	features = features[idx]

	kmeans = KMeans(num_sources)

	labels = kmeans.fit_predict(features)
	confidence = silhouette_samples(features, labels)

	return confidence.mean()