viz_utils module¶
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viz_utils.plot_2d_density_sigma_vs_error(sigma, yerror, method=None, figprefix=None)[source]¶ - Functionality to plot a 2D histogram of the distribution of
the standard deviations computed for the predictions vs. the computed errors (i.e. values of observed - predicted). The plot generated is stored in a png file.
- Parameters
sigma (numpy array) – Array with standard deviations computed.
yerror (numpy array) – Array with errors computed (observed - predicted).
method (string) – Method used to comput the standard deviations (i.e. dropout, heteroscedastic, etc.).
figprefix (string) – String to prefix the filename to store the figure generated. A ‘_density_sigma_error.png’ string will be appended to the figprefix given.
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viz_utils.plot_calibrated_std(y_test, y_pred, std_calibrated, thresC, pred_name=None, figprefix=None)[source]¶ Functionality to plot values in testing set after calibration. An estimation of the lower-confidence samples is made. The plot generated is stored in a png file.
- Parameters
y_test (numpy array) – Array with (true) observed values.
y_pred (numpy array) – Array with predicted values.
std_calibrated (numpy array) – Array with standard deviation values after calibration.
thresC (float) – Threshold to label low confidence predictions (low confidence predictions are the ones with std > thresC).
pred_name (string) – Name of data colum or quantity predicted (e.g. growth, AUC, etc.).
figprefix (string) – String to prefix the filename to store the figure generated. A ‘_calibrated.png’ string will be appended to the figprefix given.
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viz_utils.plot_calibration_interpolation(mean_sigma, error, splineobj1, splineobj2, method='', figprefix=None, steps=False)[source]¶ - Functionality to plot empirical calibration curves
estimated by interpolation of the computed standard deviations and errors. Since the estimations are very noisy, two levels of smoothing are used. Both can be plotted independently, if requested. The plot(s) generated is(are) stored in png file(s).
- Parameters
mean_sigma (numpy array) – Array with the mean standard deviations computed in inference.
error (numpy array) – Array with the errors computed from the means predicted in inference.
splineobj1 (scipy.interpolate python object) – A python object from scipy.interpolate that computes a cubic Hermite spline (PchipInterpolator) to express the interpolation after the first smoothing. This spline is a partial result generated during the empirical calibration procedure.
splineobj2 (scipy.interpolate python object) – A python object from scipy.interpolate that computes a cubic Hermite spline (PchipInterpolator) to express the mapping from standard deviation to error. This spline is generated for interpolating the predictions after a process of smoothing-interpolation-smoothing computed during the empirical calibration procedure.
method (string) – Method used to comput the standard deviations (i.e. dropout, heteroscedastic, etc.).
figprefix (string) – String to prefix the filename to store the figure generated. A ‘_empirical_calibration_interpolation.png’ string will be appended to the figprefix given.
steps (boolean) – Besides the complete empirical calibration (including the interpolating spline), also generates partial plots with only the spline of the interpolating spline after the first smoothing level (smooth1).
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viz_utils.plot_contamination(y_true, y_pred, sigma, T=None, thresC=0.1, pred_name=None, figprefix=None)[source]¶ - Functionality to plot results for the contamination model.
This includes the latent variables T if they are given (i.e. if the results provided correspond to training results). Global parameters for the normal distribution are used for shading 80% confidence interval. If results for training (i.e. T available), samples determined to be outliers (i.e. samples whose probability of membership to the heavy tailed distribution (Cauchy) is greater than the threshold given) are highlighted. The plot(s) generated is(are) stored in a png file.
- Parameters
y_true (numpy array) – Array with observed values.
y_pred (numpy array) – Array with predicted values.
sigma (float) – Standard deviation of the normal distribution.
T (numpy array) – Array with latent variables (i.e. membership to normal and heavy-tailed distributions). If in testing T is not available (i.e. None)
thresC (float) – Threshold to label outliers (outliers are the ones with probability of membership to heavy-tailed distribution, i.e. T[:,1] > thresC).
pred_name (string) – Name of data colum or quantity predicted (e.g. growth, AUC, etc.).
figprefix (string) – String to prefix the filename to store the figures generated. A ‘_contamination.png’ string will be appended to the figprefix given.
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viz_utils.plot_decile_predictions(Ypred, Ypred_Lp, Ypred_Hp, decile_list, pred_name=None, figprefix=None)[source]¶ - Functionality to plot the mean of the deciles predicted.
The plot generated is stored in a png file.
- Parameters
Ypred (numpy array) – Array with median predicted values.
Ypred_Lp (numpy array) – Array with low decile predicted values.
Ypred_Hp (numpy array) – Array with high decile predicted values.
decile_list (string list) – List of deciles predicted (e.g. ‘1st’, ‘9th’, etc.)
pred_name (string) – Name of data colum or quantity predicted (e.g. growth, AUC, etc.)
figprefix (string) – String to prefix the filename to store the figure generated. A ‘_decile_predictions.png’ string will be appended to the figprefix given.
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viz_utils.plot_density_observed_vs_predicted(Ytest, Ypred, pred_name=None, figprefix=None)[source]¶ - Functionality to plot a 2D histogram of the distribution of observed (ground truth)
values vs. predicted values. The plot generated is stored in a png file.
- Parameters
Ytest (numpy array) – Array with (true) observed values
Ypred (numpy array) – Array with predicted values.
pred_name (string) – Name of data colum or quantity predicted (e.g. growth, AUC, etc.)
figprefix (string) – String to prefix the filename to store the figure generated. A ‘_density_predictions.png’ string will be appended to the figprefix given.
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viz_utils.plot_error(y_true, y_pred, batch, file_ext, file_pre='output_dir', subsample=1000)[source]¶
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viz_utils.plot_histogram_error_per_sigma(sigma, yerror, method=None, figprefix=None)[source]¶ - Functionality to plot a 1D histogram of the distribution of
computed errors (i.e. values of observed - predicted) observed for specific values of standard deviations computed. The range of standard deviations computed is split in xbins values and the 1D histograms of error distributions for the smallest six standard deviations are plotted. The plot generated is stored in a png file.
- Parameters
sigma (numpy array) – Array with standard deviations computed.
yerror (numpy array) – Array with errors computed (observed - predicted).
method (string) – Method used to comput the standard deviations (i.e. dropout, heteroscedastic, etc.).
figprefix (string) – String to prefix the filename to store the figure generated. A ‘_histogram_error_per_sigma.png’ string will be appended to the figprefix given.
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viz_utils.plot_history(out, history, metric='loss', val=True, title=None, width=8, height=6)[source]¶