Utilities

Math Utilities

bamt.utils.MathUtils.lrts_comp(data)[source]
bamt.utils.MathUtils.mix_norm_cdf(x, weights, means, covars)[source]
bamt.utils.MathUtils.theoretical_quantile(data, n_comp)[source]
bamt.utils.MathUtils.quantile_mix(p, vals, q)[source]
bamt.utils.MathUtils.probability_mix(val, vals, q)[source]
bamt.utils.MathUtils.sum_dist(data, vals, q)[source]
bamt.utils.MathUtils.component(data, columns, method)[source]
bamt.utils.MathUtils.get_n_nearest(data, columns, corr=False, number_close=5)[source]

Returns N nearest neighbors for every column of dataframe, added into list

Parameters:

  • data (DataFrame) – Proximity matrix

  • columns (list) – df.columns.tolist()

  • corr (bool, optional) – _description_. Defaults to False.

  • number_close (int, optional) – Number of nearest neighbors. Defaults to 5.

Returns:

groups

bamt.utils.MathUtils.get_proximity_matrix(df, proximity_metric) → DataFrame[source]
Returns matrix of proximity matrix of the dataframe, dataframe must be coded first if it contains

categorical data

Parameters:

  • df (DataFrame) – data

  • df_coded (DataFrame) – same data, but coded

  • proximity_metric (str) – ‘MI’ or ‘corr’

Returns:

mutual information matrix

Return type:

df_distance

bamt.utils.MathUtils.get_brave_matrix(df_columns, proximity_matrix, n_nearest=5) → DataFrame[source]

Returns matrix Brave coeffitients of the DataFrame, requires proximity measure to be calculated

Parameters:

  • df_columns (DataFrame) – data.columns

  • proximity_matrix (DataFrame) – may be generated by get_mutual_info_score_matrix() function or correlation from scipy

  • n_nearest (int, optional) – _description_. Defaults to 5.

Returns:

DataFrame of Brave coefficients

Return type:

brave_matrix

bamt.utils.MathUtils.precision_recall(pred_net: list, true_net: list, decimal=4)[source]

Graph Utilities

bamt.utils.GraphUtils.nodes_types(data: DataFrame) → Dict[str, str][source]
Function to define the type of the node

disc - discrete node cont - continuous

Args:

data: input dataset

Returns:

dict: output dictionary where ‘key’ - node name and ‘value’ - node type

bamt.utils.GraphUtils.nodes_signs(nodes_types: dict, data: DataFrame) → Dict[str, str][source]
Function to define sign of the node

neg - if node has negative values pos - if node has only positive values

Parameters:

data (pd.DataFrame) – input dataset

Returns:

output dictionary where ‘key’ - node name and ‘value’ - sign of data

Return type:

dict

bamt.utils.GraphUtils.get_descriptor(data) → Dict[str, Dict[str, str]][source]
bamt.utils.GraphUtils.toporder(nodes: List[Type[BaseNode]], edges: List[Tuple]) → List[List[str]][source]

Function for topological sorting

class bamt.utils.GraphUtils.GraphAnalyzer(bn)[source]

Bases: object

Object to analyze DAG.

markov_blanket(node_name: str)[source]
find_family(*args)[source]

Evolutionary Utilities

Evolutionary Utilities