def important_gene_mask_tree(columns, coefs):
    """
    gene finder for tree based models since coef_ and feature_importances
    work differently.
    
    inputs
    ------
    columns: columns of df
    coefs: beta weights of lasso
    
    results
    ------
    important_genes: name of genes with weight != 0
    gene_weights: beta weights of genes
    """
    
    mask = coefs != 0
    
    gene_weights = coefs[mask]
    important_genes = columns[mask]

    return dict(zip(important_genes, gene_weights))