evomap.preprocessing ==================== .. py:module:: evomap.preprocessing .. autoapi-nested-parse:: Module for data pre-processing, including transformations between different data formats. Functions --------- .. autoapisummary:: evomap.preprocessing.diss2sim evomap.preprocessing.sim2diss evomap.preprocessing.coocc2sim evomap.preprocessing.edgelist2matrix evomap.preprocessing.edgelist2matrices evomap.preprocessing.normalize_diss_mat evomap.preprocessing.normalize_diss_mats evomap.preprocessing.expand_matrices evomap.preprocessing.calc_distances Module Contents --------------- .. py:function:: diss2sim(diss_mat, transformation='inverse', eps=0.001) Transform a dissimilarity matrix to a similarity matrix. :param diss_mat: Matrix of pairwise dissimilarities. :type diss_mat: ndarray of shape (n_samples, n_samples) :param transformation: Transformation function, either 'inverse' or 'mirror', by default 'inverse' :type transformation: str, optional :param eps: Incremental constant to avoid division by zero, by default 1e-3 :type eps: float, optional :returns: Matrix of pairwise similarities. :rtype: ndarray of shape (n_samples, n_samples) .. py:function:: sim2diss(sim_mat, transformation='inverse', eps=0.0001) Transform a similarity matrix to a dissimilarity matrix. :param sim_mat: Matrix of pairwise similarities. :type sim_mat: ndarray of shape (n_samples, n_samples) :param transformation: Transformation function, either 'inverse' or 'mirror', by default 'inverse'. 'inverse' - Transforms by taking the reciprocal of the similarity scores. 'mirror' - Transforms by reflecting the similarity scores about 0.5 (1 - similarity). :type transformation: str, optional :param eps: Incremental constant to avoid division by zero, by default 1e-4 :type eps: float, optional :returns: Matrix of pairwise dissimilarities. :rtype: ndarray of shape (n_samples, n_samples) .. py:function:: coocc2sim(coocc_mat) Transform a matrix with co-occurrence counts to a similarity matrix. :param coocc_mat: Matrix of co-occurrence counts. Assumes a non-negative, symmetric matrix where the diagonal can be ignored (usually representing self-co-occurrence). :type coocc_mat: ndarray of shape (n_samples, n_samples) :returns: Matrix of pairwise similarities, normalized such that each element is a proportion of the maximum co-occurrence for that row. :rtype: ndarray of shape (n_samples, n_samples) .. rubric:: Notes The function sets the diagonal to zero to prevent self-similarity from skewing the results. If a row's total co-occurrence is zero, it sets the entire row's similarity to zero to avoid division by zero. .. py:function:: edgelist2matrix(df, score_var, id_var_i, id_var_j, time_var=None, time_selected=None) Transform an edgelist to a relationship matrix. :param df: Data containing the edgelist. Each row should include a pair. Must contain two id variables and a score variable. Can optionally include a time variable. :type df: DataFrame :param score_var: The score variable. :type score_var: string :param id_var_i: The first id variable. :type id_var_i: string :param id_var_j: The second id variable. :type id_var_j: string :param time_var: The time variable, by default None. :type time_var: string, optional :param time_selected: The selected time, by default None. :type time_selected: int, optional :returns: * **S** (*ndarray of shape (n_samples, n_samples)*) -- A matrix of pairwise relationships. * **ids** (*ndarray*) -- Identifiers for each element of the matrix. :raises ValueError:: If required columns are missing in the DataFrame. .. py:function:: edgelist2matrices(df, score_var, id_var_i, id_var_j, time_var) Transform a time-indexed edgelist into a sequence of relationship matrices. :param df: Data containing the edgelist. Each row should include a pair and must contain two id variables, a score variable, and a time variable. :type df: DataFrame :param score_var: The score variable used to assign values in the matrix. :type score_var: string :param id_var_i: The first id variable corresponding to rows in the matrix. :type id_var_i: string :param id_var_j: The second id variable corresponding to columns in the matrix. :type id_var_j: string :param time_var: The time variable used to split the data into different matrices. :type time_var: string :returns: * **S_t** (*list of ndarray*) -- A list of relationship matrices, each corresponding to a different time period. * **ids_t** (*ndarray*) -- Array of identifiers for each element of the matrices. :raises ValueError:: If the DataFrame is missing any required columns or if there are no valid entries for any time period. .. py:function:: normalize_diss_mat(D) Normalize a dissimilarity matrix by the maximum dissimilarity observed in the matrix. :param D: A dissimilarity matrix. :type D: ndarray of shape (n_samples, n_samples) :returns: Normalized dissimilarity matrix. :rtype: ndarray of shape (n_samples, n_samples) :raises ValueError: If the input matrix is not square or if the maximum dissimilarity is zero. .. py:function:: normalize_diss_mats(D_ts) Normalize a sequence of dissimilarity matrices by a common factor (the maximum dissimilarity within the sequence). :param D_ts: Sequence of dissimilarity matrices. :type D_ts: list of ndarray, each of shape (n_samples, n_samples) :returns: Sequence of dissimilarity matrices, normalized by the maximum dissimilarity within the input sequence. :rtype: list of ndarray :raises ValueError: If any matrix is not square or if the list is empty. .. py:function:: expand_matrices(X_ts, labels_ts) Expand a list of similarity matrices (X_ts) to equal shape and calculate inclusion vectors. :param X_ts: List of similarity matrices for each time point. :type X_ts: list of ndarray :param labels_ts: List of labels corresponding to each matrix in X_ts. :type labels_ts: list of list :returns: Contains a list of expanded similarity matrices, inclusion vectors, and all labels. :rtype: tuple .. py:function:: calc_distances(X, metric='euclidean') Calculate matrix of pairwise distances among the rows of an input matrix. :param X: Input matrix containing samples for which pairwise distances will be calculated. :type X: ndarray of shape (n_samples, n_dims) :param metric: The distance metric to use. Can be any of those supported by `scipy.spatial.distance.pdist`, such as 'euclidean', 'cityblock', 'cosine', etc. Defaults to 'euclidean'. :type metric: str, optional :returns: A matrix of pairwise distances, where each element (i, j) is the distance between the i-th and j-th rows of the input matrix X according to the specified metric. :rtype: ndarray of shape (n_samples, n_samples) :raises ValueError: If the metric specified is not supported by `scipy.spatial.distance.pdist`.