Package schrodinger :: Package application :: Package desmond :: Module correlation_tau

Module correlation_tau

This program computes the average correlation coefficient and the kendall tau rank coefficient between experiment and prediction samples. Samples are randomly drawn from gaussian distribution centered on each experimental data point with given error.

The algorithm used in this program is based on the work by Scott P. Brown, Steven W. Muchmore, Philip J. Hajduk Drug Discovery Today, Vol. 14, No. 7-8., pp. 420-427

Functions

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predict_kendall_tau(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)
Computes the average Kendall tau rank correlation coefficient between experiment and prediction samples.

predict_correlation(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)
Computes the average correlation coefficient between experiment and prediction samples.

_prepare_exp_pre_sample(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)
:param experiment: sequence of experiment data :param experiment_sigma: experimental error :param prediction_sigma: prediction error :param num_sample: number of samples :return: experiment_sample, prediction_sample

compute_rmse(experiment, prediction)
Computes root mean square error between experiment and prediction.

Variables

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__package__ = 'schrodinger.application.desmond'

Function Details

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predict_kendall_tau(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)

Computes the average Kendall tau rank correlation coefficient between experiment and prediction samples. num_sample independent data for each experiment and prediction are sampled from gaussian distribution with experiment_sigma and prediction_sigma error.

:param experiment: sequence of experiment data :param experiment_sigma: experimental error :param prediction_sigma: prediction error :param num_sample: number of samples :return: average_tau, sigma_tau

predict_correlation(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)

Computes the average correlation coefficient between experiment and prediction samples. num_sample independent data for each experiment and prediction are sampled from gaussian distribution with experiment_sigma and prediction_sigma error.

compute_rmse(experiment, prediction)

Computes root mean square error between experiment and prediction. Averages of experiment and prediction are aligned before RMSE computation.

:param experiment: sequence of experiment data :param prediction: sequence of prediction data :return: root_mean_square_error between experiment and prediction