bioneuralnet.utils.preprocess — BioNeuralNet 1.0.4 documentation

# bioneuralnet.utils.preprocess

Functions

`clean_inf_nan`(df) | Replace infinite values with NaN, impute NaNs with the column median, and drop zero-variance columns.
`get_logger`(name) | Retrieves a global logger configured to write to 'bioneuralnet.log' at the project root.
`multipletests`(pvals[, alpha, method, ...]) | Test results and p-value correction for multiple tests
`network_remove_high_zero_fraction`(network[, ...]) | Remove rows and columns from adjacency matrix where the fraction of zero entries is higher than the threshold.
`network_remove_low_variance`(network[, threshold]) | Remove rows and columns from adjacency matrix where the variance is below a threshold.
`preprocess_clinical`(X, y[, top_k, scale, ...]) | Preprocess clinical data, handling numeric and categorical features, cleaning, optional scaling, and selecting top features by RandomForest importance.
`prune_network`(adjacency_matrix[, ...]) | Prune a network based on a weight threshold, removing nodes with weak connections.
`prune_network_by_quantile`(adjacency_matrix) | Prune a network by removing edges below a quantile-based weight threshold and dropping isolated nodes.
`select_top_k_correlation`(X[, y, top_k]) | Select the top k features by correlation, either supervised (with respect to y) or unsupervised (redundancy minimization).
`select_top_k_variance`(df[, k, ddof]) | Select the top k features with the highest variance.,
`select_top_randomforest`(X, y[, top_k, seed]) | Select the top k features using RandomForest feature importances.
`top_anova_f_features`(X, y, max_features[, ...]) | Select top features based on ANOVA F-test (with false recovery rate correction).

bioneuralnet.utils.preprocess.clean_inf_nan(df: DataFrame) → DataFrame

  Replace infinite values with NaN, impute NaNs with the column median, and drop zero-variance columns.

  Parameters:

    df (-) – Input DataFrame containing numeric columns.

  Returns:

    Cleaned DataFrame with no infinite or NaN values and no zero-variance columns.

  Return type:

    - pd.DataFrame

bioneuralnet.utils.preprocess.network_remove_high_zero_fraction(network: DataFrame, threshold: float = 0.95) → DataFrame

  Remove rows and columns from adjacency matrix where the fraction of zero entries is higher than the threshold.

  Parameters:

    - network (pd.DataFrame) – Adjacency matrix.

    - threshold (float) – Zero-fraction threshold.

  Returns:

    Filtered adjacency matrix.

  Return type:

    pd.DataFrame

bioneuralnet.utils.preprocess.network_remove_low_variance(network: DataFrame, threshold: float = 1e-06) → DataFrame

  Remove rows and columns from adjacency matrix where the variance is below a threshold.

  Parameters:

    - network (pd.DataFrame) – Adjacency matrix.

    - threshold (float) – Variance threshold.

  Returns:

    Filtered adjacency matrix.

  Return type:

    pd.DataFrame

bioneuralnet.utils.preprocess.preprocess_clinical(X: DataFrame, y: Series, top_k: int = 10, scale: bool = False, ignore_columns=None) → DataFrame

  Preprocess clinical data, handling numeric and categorical features, cleaning, optional scaling, and selecting top features by RandomForest importance.

  Parameters:

    - X (-) – Clinical feature matrix (samples x features) including numeric and categorical columns.

    - y (-) – Target values; single-column DataFrame or Series of length n_samples.

    - top_k (-) – Number of features to select based on importance.

    - scale (-) – If True, scale numeric features using RobustScaler; default is False.

    - ignore_columns (-) – List of columns to ignore during preprocessing; default is None.

  Returns:

    Subset of the original features with the selected top_k features plus ignored columns.

  Return type:

    - pd.DataFrame

bioneuralnet.utils.preprocess.prune_network(adjacency_matrix, weight_threshold=0.0)

  Prune a network based on a weight threshold, removing nodes with weak connections.

  Parameters:

    - adjacency_matrix (-) – The adjacency matrix of the network.

    - weight_threshold (-) – Minimum weight to keep an edge (default: 0.0).

  Return type:

    - pd.DataFrame

bioneuralnet.utils.preprocess.prune_network_by_quantile(adjacency_matrix, quantile=0.5)

  Prune a network by removing edges below a quantile-based weight threshold and dropping isolated nodes.

  Parameters:

    - adjacency_matrix (-) – Weighted adjacency matrix (nodes x nodes).

    - quantile (-) – Quantile in [0,1] to compute weight threshold; default is 0.5.

  Returns:

    Pruned adjacency matrix with edges below the quantile threshold removed.

  Return type:

    - pd.DataFrame

bioneuralnet.utils.preprocess.select_top_k_correlation(X: DataFrame, y: Series = None, top_k: int = 1000) → DataFrame

  Select the top k features by correlation, either supervised (with respect to y) or unsupervised (redundancy minimization).

  Parameters:

    - X (-) – Numeric feature matrix (samples x features).

    - y (-) – Target values for supervised selection; if None, performs unsupervised selection.

    - top_k (-) – Number of features to select.

  Returns:

    Subset of X containing the selected features.

  Return type:

    - pd.DataFrame

  Note

  - Correlation computation can be expensive for large datasets.

bioneuralnet.utils.preprocess.select_top_k_variance(df: DataFrame, k: int = 1000, ddof: int = 0) → DataFrame

  Select the top k features with the highest variance.,

  Parameters:

    - df (-) – Input DataFrame; non-numeric columns will be ignored.

    - k (-) – Number of top-variance features to select.

    - ddof (-) – Delta degrees of freedom for varianceg calculation; default is 0.

  Returns:

    DataFrame containing only the top k features by variance.

  Return type:

    - pd.DataFrame

bioneuralnet.utils.preprocess.select_top_randomforest(X: DataFrame, y: Series, top_k: int = 1000, seed: int = 119) → DataFrame

  Select the top k features using RandomForest feature importances.

  Parameters:

    - X (-) – Numeric feature matrix (samples x features); must contain only numeric columns.

    - y (-) – Target values; single-column DataFrame or Series.

    - top_k (-) – Number of features to select.

    - seed (-) – Random seed for the RandomForest model; default is 119.

  Returns:

    Subset of X containing the selected top_k features by importance.

  Return type:

    - pd.DataFrame

bioneuralnet.utils.preprocess.top_anova_f_features(X: DataFrame, y: Series, max_features: int, alpha: float = 0.05, task: str = 'classification') → DataFrame

  Select top features based on ANOVA F-test (with false recovery rate correction). This function is suitable for both classification and regression tasks.

  Parameters:

    - X (-) – Numeric feature matrix (samples x features).

    - y (-) – Target vector; categorical for classification or continuous for regression.

    - max_features (-) – Maximum number of features to return.

    - alpha (-) – Significance threshold for false recovery rate correction; default is 0.05.

    - task (-) – ‘classification’ to use f_classif or ‘regression’ to use f_regression.

  Returns:

    Subset of X with the selected features, padded if necessary.

  Return type:

    - pd.DataFrame