give common heuristics

[dpastoor]

Summary

representative example via PsN:

Successful minimization [ OK ]
No rounding errors [ OK ]
No zero gradients [ OK ]
No final zero gradients [ OK ]
Hessian not reset [ OK ]
Parameter(s) near boundary [ WARNING ]
OMEGA(2,2) 0.000112 0
Covariance step [ OK ]
Large condition number [ WARNING ]
Correlations [ OK ]

To exercise summary:
from: github.com/metrumresearchgroup/babylon/testdata/example-models/nonmem/BQL
bbi summary --json 2

Tests

• summary_test.go
  • TestSummary

[dpastoor]

• Successful minimization [ OK ]

search for:

0MINIMIZATION SUCCESSFUL

• No rounding errors [ OK ]

• No zero gradients [ OK ]

• No final zero gradients [ OK ]

• Hessian not reset [ OK ]
  sample line: CUMULATIVE NO. OF FUNC. EVALS.: 598 RESET HESSIAN, TYPE I
  search text: RESET HESSIAN

• Parameter(s) near boundary [ WARNING ]
  sample line: 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY
  search text: PARAMETER ESTIMATE IS NEAR ITS BOUNDARY

• OMEGA(2,2) 0.000112 0
  I don't think this should have been copied from above?

• Covariance step [ OK ]
  search text:
  0COVARIANCE STEP OMITTED: NO

• Large condition number [ WARNING ]
  One way:
  The ratio of the largest eigenvalue to the smallest eigenvalue, referred to as the condition
  number should be less than 1000
  search text: EIGENVALUES OF COR MATRIX OF ESTIMATE
  ... a few lines later:
  2.53E-01 4.29E-01 6.19E-01 7.76E-01 8.79E-01 9.30E-01 1.19E+00 1.31E+00 2.61E+00
  This link leads me to believe the condition number must be calculated:
  Condition Number from get_summary() Appears Inaccurate UUPharmacometrics/xpose#128

• Correlations [ OK ]
  Maybe check for large numbers (close to 1) in the off-diagonal values of the correlation matrix?
  search text: CORRELATION MATRIX OF ESTIMATE

[david-lyder]

After change:

"run_heuristics": {
"large_condition_number": "HeuristicFalse",
"correlations_ok": "HeuristicTrue",
"has_final_zero_gradient": "HeuristicFalse",
"minimization_successful": "HeuristicTrue"
},

[david-lyder]

The above heuristic output is considered complete for version 2.0.0
Further enhancements will be addressed based on feedback from the scientists.