added file to compute and store the true fit of an optimization set u…

…sing the reference fitness rules

compute_true_fit.py

@@ -0,0 +1,74 @@
+
+'''
+
+Computes the *true* degree of fit to the training data, that is, without any
+augmentation.
+
+Usage:
+
+python compute_true_fit.py <path to output directory>
+
+Evaluate after calling gather_output.py.
+
+Note: since the reference rules include no one-sided penalty terms, they have
+been omitted here.  This will be an issue if the REFERENCE_RULES are changed to
+include one-sided penalties.
+
+'''
+
+from __future__ import division
+
+import os
+
+from itertools import izip
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+import constants
+import problems
+import fitting
+
+REFERENCE_RULES = problems.DEFINITIONS['all_scaled']
+
+def get_absolute_fit_residuals(pars):
+	(fm, fv, fe) = fitting.build_fitting_tensors(*REFERENCE_RULES)
+
+	residuals = fm.dot(pars) - fv[:, None]
+
+	return residuals
+
+def get_relative_fit_residuals(pars):
+	all_residuals = []
+
+	for (fm, fv, fe) in fitting.build_relative_fitting_tensor_sets(*REFERENCE_RULES):
+		raw_residuals = fm.dot(pars) - fv[:, None]
+
+		medians = np.median(raw_residuals, 0)
+
+		residuals = raw_residuals - medians[None, :]
+
+		all_residuals.append(residuals)
+
+	return np.row_stack(all_residuals)
+
+def load_pars(directory):
+	pars = np.load(os.path.join(directory, 'pars.npy'))
+
+	return pars
+
+def compute_fitness(pars):
+	return (
+		np.abs(get_absolute_fit_residuals(pars)).sum(0)
+		+ np.abs(get_relative_fit_residuals(pars)).sum(0)
+		)
+
+if __name__ == '__main__':
+	import sys
+
+	source = sys.argv[1]
+
+	np.save(
+		os.path.join(source, 'fit.npy'),
+		compute_fitness(load_pars(source))
+		)