Source code for mlconfound.plot

import dot2tex
import graphviz
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

from .stats import ResultsPartiallyConfounded, ResultsFullyConfounded

def _pval_to_str(pval, alpha=0.05, floor=0.0001):
    if pval is None:
        return ''
    elif pval > alpha:
        return 'p=' + str(np.round(pval, 2))
    elif pval > floor:
        return 'p=' + str(np.round(pval, np.ceil(-np.log10(pval)).astype(int)))
        return 'p<' + str(floor)

[docs]def plot_null_dist(confound_test_results, **kwargs): """ Plot the histogram of the permutation-based null distribution of the confounder test. Parameters ---------- confound_test_results: namedtuple The object returned by `test_partially_confounded` or `test_fully_confounded`. kwargs : dict Additional named argumnets, passed to seaborn.histplot. Returns -------- matplotlib.axes.Axes The matplotlib axes containing the plot. See Also --------- plot_graph """ if not hasattr(confound_test_results, 'null_distribution'): raise RuntimeError("No null distribution data is available. " "Re-run 'confound_test' with 'return_null_dist=True'!") g = sns.histplot(confound_test_results.null_distribution, **kwargs) g.set(xlabel='R2(y^,c*)', ylabel='count') g.set_title('null distribution') plt.axvline(confound_test_results.r2_yhat_c, color='red') return g
[docs]def plot_graph(confound_test_results, y_name='y', yhat_name='<y&#770;>', c_name='c', outfile_base=None, precision=3): """ Plot confounder test results as a graph depicting the involved variables. Parameters ---------- confound_test_results : namedtuple The object returned by `test_partially_confounded` or `test_fully_confounded`. y_name: str Name of the target variable. yhat_name: str Name for the model predictions. c_name: str Name of the confounder variable. outfile_base: str Path for output files, without extension (None: figure not saved). precision: int Precision for r squared values. Returns ------- dot The graphviz object to plot. See Also -------- plot_null_dist """ if isinstance(confound_test_results, ResultsPartiallyConfounded): mode = 'partial' else: mode = 'full' return plot_r2_graph(confound_test_results.r2_y_c, confound_test_results.r2_yhat_c, confound_test_results.r2_y_yhat, confound_test_results.p, y_name=y_name, yhat_name=yhat_name, c_name=c_name, mode=mode, outfile_base=outfile_base, precision=precision)
def plot_r2_graph(r2_y_c, r2_yhat_c, r2_y_yhat, p_yhat_c=None, y_name='y', yhat_name='yhat', c_name='c', mode='partial', outfile_base=None, precision=3, alpha=0.05, minp=0.0001): dot = graphviz.Graph() dot.attr(rankdir='LR') if mode != 'partial' and mode != 'full': raise AttributeError("Mode must be either 'partial' or 'full'.") if p_yhat_c < alpha: star = '*' else: star = '' pvalstr = ' (' + _pval_to_str(p_yhat_c, alpha, minp) + star + ')' dot.node('c', label=c_name) dot.node('y', label=y_name) dot.node('yhat', label=yhat_name) if mode == 'partial': dot.edge('c', 'yhat', label=str(np.round(r2_yhat_c, precision)) + pvalstr, style="dashed") else: dot.edge('c', 'yhat', label=str(np.round(r2_yhat_c, precision))) dot.edge('c', 'y', label=str(np.round(r2_y_c, precision))) if mode == 'full': dot.edge('y', 'yhat', label=str(np.round(r2_y_yhat, precision)) + pvalstr, style="dashed") else: dot.edge('y', 'yhat', label=str(np.round(r2_y_yhat, precision))) if outfile_base is not None: dot.render(filename=outfile_base + '.dot') # saves dot and pdf tex_code = dot2tex.dot2tex(dot.source, format='tikz', crop=True) with open(outfile_base + '.tex', "w") as text_file: text_file.write(tex_code) return dot