skfolio.utils.stats.cs_pearson_correlation#
- skfolio.utils.stats.cs_pearson_correlation(a, b, weights=None, axis=0, min_count=3, eps=1e-12)[source]#
Weighted cross-sectional Pearson correlation.
Computes the weighted Pearson correlation between a and b along
axis. All other dimensions are treated as independent batch dimensions over which the computation is vectorized.For vectors \(a\) and \(b\) with weights \(w\):
\[\rho = \frac{ \sum_n w_n \,(a_n - \bar a)\,(b_n - \bar b) }{ \sqrt{\sum_n w_n \,(a_n - \bar a)^2}\; \sqrt{\sum_n w_n \,(b_n - \bar b)^2} }\]where \(\bar a = \sum_n w_n a_n / \sum_n w_n\) (and likewise for \(\bar b\)).
- Parameters:
- andarray
First array.
- bndarray
Second array, broadcastable to the same shape as a.
- weightsndarray, optional
Non-negative weights, broadcastable to a along
axis.Noneuses equal weights. Non-finite and zero weights are excluded from weighted correlations.- axisint, default=0
The cross-sectional axis along which correlation is computed.
- min_countint, default=3
Minimum number of effective observations along
axis. Withoutweights, this is the number of jointly finite observations. Withweights, this is the number of jointly finite observations with finite strictly positive weight.- epsfloat, default=1e-12
Denominator threshold below which
NaNis returned to guard against near-constant vectors.
- Returns:
- corrfloat or ndarray
Scalar when inputs are 1D, otherwise an array with
axisremoved.