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Examples/Sigmoid/SigmoidTest.py

@@ -1,9 +1,9 @@
 import numpy as np
 import FoKL
-from FoKL import getKernels
 from FoKL import FoKLRoutines
+from FoKL import getKernels
 
-#%% Setting input parameters
+# Setting input parameters
 
 #inputs
 X = np.loadtxt('X.csv',dtype=float,delimiter=',')
@@ -25,7 +25,6 @@
 
 data = np.reshape(data_load, (m*n,1), order='F')
 
-# load basis functions
 phis = getKernels.sp500()
 
 # a
@@ -52,11 +51,100 @@
 threshstda = 0.5
 threshstdb = 2
 aic = True
+
 # define model
 model = FoKLRoutines.FoKL(phis, relats_in, a, b, atau, btau, tolerance, draws, gimmie, way3, threshav, threshstda, threshstdb, aic)
-
-#%% Running emulator_Xin routine and visualization, fit model to data
+"""
+        initialization inputs:
+
+        'relats' is a boolean matrix indicating which terms should be excluded
+        from the model building; for instance if a certain main effect should be
+        excluded relats will include a row with a 1 in the column for that input
+        and zeros elsewhere; if a certain two way interaction should be excluded
+        there should be a row with ones in those columns and zeros elsewhere
+        to exclude no terms 'relats = np.array([[0]])'. An example of excluding
+        the first input main effect and its interaction with the third input for
+        a case with three total inputs is:'relats = np.array([[1,0,0],[1,0,1]])'
+
+        'phis' are a data structure with the spline coefficients for the basis
+        functions, built with 'spline_coefficient.txt' and 'splineconvert' or
+        'spline_coefficient_500.txt' and 'splineconvert500' (the former provides
+        25 basis functions: enough for most things -- while the latter provides
+        500: definitely enough for anything)
+
+        'a' and 'b' are the shape and scale parameters of the ig distribution for
+        the observation error variance of the data. the observation error model is
+        white noise choose the mode of the ig distribution to match the noise in
+        the output dataset and the mean to broaden it some
+
+        'atau' and 'btau' are the parameters of the ig distribution for the 'tau
+        squared' parameter: the variance of the beta priors is iid normal mean
+        zero with variance equal to sigma squared times tau squared. tau squared
+        must be scaled in the prior such that the product of tau squared and sigma
+        squared scales with the output dataset
+
+        'tolerance' controls how hard the function builder tries to find a better
+        model once adding terms starts to show diminishing returns. a good
+        default is 3 -- large datasets could benefit from higher values
+
+        'draws' is the total number of draws from the posterior for each tested
+        model
+
+        'draws' is the total number of draws from the posterior for each tested
+        
+         'threshav' is a threshold for proposing terms for elimination based on
+         their mean values (larger thresholds lead to more elimination)
+        
+         'threshstda' is a threshold standard deviation -- expressed as a fraction 
+         relative to the mean -- that pairs with 'threshav'.
+         terms with coefficients that are lower than 'threshav' and higher than
+         'threshstda' will be proposed for elimination (elimination will happen or not 
+         based on relative BIC values)
+        
+         'threshstdb' is a threshold standard deviation that is independent of the
+         mean value of the coefficient -- all with a standard deviation (fraction 
+         relative to mean) exceeding
+         this value will be proposed for elimination
+         
+        'gimmie' is a boolean causing the routine to return the most complex
+        model tried instead of the model with the optimum bic
+
+        'aic' is a boolean specifying the use of the aikaike information
+        criterion 
+    """
+# Running emulator routine and visualization, fit model to data
 betas, mtx, evs = model.fit(inputs, data)
-
-meen, bounds, rmse = model.coverage3(inputs, data, 1000, 1)
-
+"""
+    inputs: 
+        'inputs' - normalzied inputs
+        
+        'data' - results
+    
+    outputs:
+         'betas' are a draw from the posterior distribution of coefficients: matrix, with
+         rows corresponding to draws and columns corresponding to terms in the GP
+        
+         'mtx' is the basis function interaction matrix from the
+         best model: matrix, with rows corresponding to terms in the GP (and thus to the 
+         columns of 'betas' and columns corresponding to inputs. a given entry in the 
+         matrix gives the order of the basis function appearing in a given term in the GP.
+         all basis functions indicated on a given row are multiplied together.
+         a zero indicates no basis function from a given input is present in a given term
+        
+         'ev' is a vector of BIC values from all of the models
+         evaluated
+"""
+
+meen, bounds, rmse = model.coverage3(inputs, data, draws, 1)
+"""
+    inputs:
+        'inputs' - normalized test inputs
+        'data' - test set results
+        'draws' - number of beta models used from model.fit()
+        plotting binary - 1 = show plot, 0 = do not show plot
+        
+    outputs:
+        'meen' - indexed predicted value
+        'bounds' - 95% confidence interval for each point
+        'rmse' - root mean square error
+"""