pdmlabs.evaluation.vus.utils.stat_models

A collection of statistical models code copied from pyod documentation yzhao062/pyod

Functions

pairwise_distances_no_broadcast(X, Y)	Utility function to calculate row-wise euclidean distance of two matrix.
pearsonr_mat(mat[, w])	Utility function to calculate pearson matrix (row-wise).
wpearsonr(x, y[, w])	Utility function to calculate the weighted Pearson correlation of two samples.

pdmlabs.evaluation.vus.utils.stat_models.pairwise_distances_no_broadcast(X, Y)

Utility function to calculate row-wise euclidean distance of two matrix. Different from pair-wise calculation, this function would not broadcast. For instance, X and Y are both (4,3) matrices, the function would return a distance vector with shape (4,), instead of (4,4). :param X: First input samples :type X: array of shape (n_samples, n_features) :param Y: Second input samples :type Y: array of shape (n_samples, n_features)

Returns:

distance – Row-wise euclidean distance of X and Y

Return type:

array of shape (n_samples,)

pdmlabs.evaluation.vus.utils.stat_models.pearsonr_mat(mat, w=None)

Utility function to calculate pearson matrix (row-wise). :param mat: Input matrix. :type mat: numpy array of shape (n_samples, n_features) :param w: Weights. :type w: numpy array of shape (n_features,)

Returns:

pear_mat – Row-wise pearson score matrix.

Return type:

numpy array of shape (n_samples, n_samples)

pdmlabs.evaluation.vus.utils.stat_models.wpearsonr(x, y, w=None)

Utility function to calculate the weighted Pearson correlation of two samples. See https://stats.stackexchange.com/questions/221246/such-thing-as-a-weighted-correlation for more information :param x: Input x. :type x: array, shape (n,) :param y: Input y. :type y: array, shape (n,) :param w: Weights w. :type w: array, shape (n,)

Returns:

scores – Weighted Pearson Correlation between x and y.

Return type:

float in range of [-1,1]