NET-PRED: Make predictions for test cases using neural network model. Net-pred prints guesses at the target values for a set of test cases. Guesses are as defined by a network or set of networks. If the true targets are known, performance of the guesses can also be evaluated. Inputs can be printed as well. If Annealed Importance Sampling was used, the marginal likelihood of the model will be displayed. Usage: net-pred options { log-file range } [ / test-inputs [ test-targets ] ] The final optional arguments give the source of inputs and targets for the cases to look at; they default to the test data specification in the first log file given. The networks to use in making guesses are taken from the records with the given ranges of indexes in the given log files. The outputs of all these networks are combined to give a single guess for each case. The networks should all have the same architecture, and use the same data specifications. An index range can have one of the forms "[low][:[high]][%mod]" or "[low][:[high]]+num", or one of these forms preceded by "@". When "@" is present, "low" and "high" are given in terms of cpu time, otherwise they are iteration numbers. When just "low" is given, only that index is used. If the colon is included, but "high" is not, the range extends to the highest index in the log file. The "mod" form allows networks to be selected whose iteration numbers are multiples of "mod", with the default being "mod" of one. The "num" form allows the total number of networks used to be specified; they are distributed as evenly as possible within the specified range. Note that it is possible that the number of networks used in the end may not equal this number, if records with some indexes are missing. The 'options' argument consists of one or more of the following letters: i Display the input values for each case t Display the target values for each case r Use the raw form of the target values, before transformation p Display the log probability of the true targets (to base e) m Display the guess based on the mode, and whether it is in error n Display the guess based on the mean, and its squared error d Display the guess based on the median, and its absolute error D Display the guess based on the mean of the median for each iteration. This is mostly useful to get an accurate median for one network. q Display the 10% and 90% quantiles of the predictive distributions for the targets. Note that these distributions include the noise. Q Display the 1% and 99% quantiles of the predictive distributions. b Suppress headings and averages - just bare numbers for each case. The numbers are printed in exponential format, to high precision. B Bare numbers, but with blank lines whenever first input changes a Display only average log probabilities and errors, suppressing the results for individual cases (makes sense only in combination with one or more of 'p', 'm', 'n', and 'd', and not with 'i' or 't') E Display the expected error for test cases, based on the predictive probabilities, as well as the actual error (if targets are known). Currently implemented only for "binary" data. 0-9 If any of these characters are used, output connections from hidden layers other than those named are suppressed. Input-output connections and output biases are also suppressed. This option is useful in seeing the components of an additive network model. (But note that if the identity of a component can change during a run, this option will be meaningful only when the predictions are based on a single iteration.) Some of these options are illegal for some data models. The illegal combinations are marked with an 'X' in the following table: binary class real-valued survival no-model r X X p X m X X X n d/D X X q/Q X X Furthermore, the 'a' option is incompatible with 'b', 'i', 't', or 'q', and the 't', 'p', and 'a' options may be used only if the true targets are given. The errors for individual cases are also displayed only if the true targets are known. The 'n' option for class models displays the mean probabilities for each class, and computes a single figure for squared error that is the sum of the squares of the differences between these probabilities and the indicator variables that are one for the right class and zero for the wrong classes. The median and quantiles for the 'd', 'q', and 'Q' options are calculated by Monte Carlo, using a sample consisting of 101 points from each network's target distribution. These computations are not allowed if the data is weighted, as produced with Annealed Importance Sampling. These Monte Carlo estimates are found using a random number stream initialized by setting the seed to one at the start of the program. Accuracy can be increased by repeating the same log-file/range combination several times, effectively increasing the sample size use. The mean median found using 'D' is calculated exactly, without Monte Carlo. This option is mostly useful for survival models, when a single iteration specified, since it then produces an accurate value for the median prediction with that one network (more accurate than would be obtained using 'd'). No error value is produced to go with the mean median. Each average performance figure is accompanied by +- its standard error (as long as there is more than one test case). If Annealed Importance Sampling was done, the estimated marginal likelihood is also accompanied by an estimated standard error, as long as the "adjusted sample size" (defined in mc-ais.doc) is at least two. Note that the actual error might be much larger than the estimated standard error if the AIS runs have missed an important part of the distribution. For survival data, if the survival time in a test case is censored, the censoring time (coded as negative) is used as the time of death for the purpose of computing squared or absolute error. This is not very meaningful. If only inputs and targets are to be displayed (no predictions), one may give just a single log file with no range. Otherwise, at least one network must be specified. Copyright (c) 1995-2004 by Radford M. Neal