def simple_plots(d): # TO y_cov = d.y_cov y_dot_cov = d.y_dot_cov y_dot_sign_cov = d.y_dot_sign_cov vars = [ ('y', y_cov, {}), ('y_dot', y_dot_cov, {}), ('y_dot_sign', y_dot_sign_cov, {}) ] # # I = numpy.eye(y_cov.shape[0]) # r = Report() f = r.figure(cols=3) for var in vars: label = var[0] cov = var[1] corr = cov2corr(cov, zero_diagonal=False) corr_z = cov2corr(cov, zero_diagonal=True) n1 = r.data("cov_%s" % label, cov).display('posneg') n2 = r.data("corr_%s" % label, corr).display('posneg') n3 = r.data("corrz_%s" % label, corr_z).display('posneg') f.sub(n1, 'Covariance of %s' % label) f.sub(n2, 'Correlation of %s ' % label) f.sub(n3, 'Correlation of %s (zeroing diagonal)' % label) return r
def hist_plots(d): # TO vars = [ ('C', d.C, {}), ('y', cov2corr(d.y_cov, False), {}), ('y_dot', cov2corr(d.y_dot_cov, False), {}), ('y_dot_sign', cov2corr(d.y_dot_sign_cov, False), {}) ] r = Report() f = r.figure(cols=5) for var in vars: label = var[0] x = var[1] nid = "hist_%s" % label with r.data_pylab(nid) as pylab: pylab.hist(x.flat, bins=128) f.sub(nid, 'histogram of correlation of %s' % label) order = scale_score(x) r.data('order%s' % label, order).display('posneg').add_to(f, 'ordered') nid = "hist2_%s" % label with r.data_pylab(nid) as pylab: pylab.plot(x.flat, order.flat, '.', markersize=0.2) pylab.xlabel(label) pylab.ylabel('order') f.sub(nid, 'histogram of correlation of %s' % label) h = create_histogram_2d(d.C, x, resolution=128) r.data('h2d_%s' % label, numpy.flipud(h.T)).display('scale').add_to(f) return r
def iterations_plots(d): r = Report('algorithmic_results') # dummy = d.C ** 5 # -- perfect! dummy = numpy.maximum(0, d.C ** 7) results = cbc(dummy, num_iterations=5, ground_truth=d) r.add_child(plot_results('dummy', results)) # y_corr = cov2corr(d.y_cov, False) results = cbc(y_corr, num_iterations=5, ground_truth=d) r.add_child(plot_results('y_corr', results)) # # y_dot_corr = cov2corr(d.y_dot_cov, False) # results = cbc(y_dot_corr, num_iterations=5, ground_truth=d) # r.add_child(plot_results('y_dot_corr', results)) y_dot_sign_corr = cov2corr(d.y_dot_sign_cov, False) results = cbc(y_dot_sign_corr, num_iterations=5, ground_truth=d) r.add_child(plot_results('y_dot_sign_corr', results)) return r