You can test out this function at the REPL.

    1. SPAM> (result-type '(:FILE #p"foo" :type ham :classification ham :score 0))
    2. CORRECT
    3. SPAM> (result-type '(:FILE #p"foo" :type spam :classification spam :score 0))
    4. CORRECT
    6. FALSE-POSITIVE
    7. SPAM> (result-type '(:FILE #p"foo" :type spam :classification ham :score 0))
    8. FALSE-NEGATIVE
    9. SPAM> (result-type '(:FILE #p"foo" :type ham :classification unsure :score 0))
    10. SPAM> (result-type '(:FILE #p"foo" :type spam :classification unsure :score 0))
    11. MISSED-SPAM

    With those functions, you can easily use the list and sequence manipulation functions I discussed in Chapter 11 to extract and count particular kinds of results.

    1. SPAM> (count-if #'false-positive-p *results*)
    2. 6
    3. SPAM> (remove-if-not #'false-positive-p *results*)
    4. ((:FILE #p"ham/5349" :TYPE HAM :CLASSIFICATION SPAM :SCORE 0.9999983107355541d0)
    5.  (:FILE #p"ham/2746" :TYPE HAM :CLASSIFICATION SPAM :SCORE 0.6286468956619795d0)
    7.  (:FILE #p"ham/7785" :TYPE HAM :CLASSIFICATION SPAM :SCORE 0.9542788587998488d0)
    8.  (:FILE #p"ham/10581" :TYPE HAM :CLASSIFICATION SPAM :SCORE 0.9999924537959615d0))

    This function will give output like this when passed a list of results generated by test-classifier:

    1. SPAM> (analyze-results *results*)
    2. Total:               3761 : 100.00%
    3. Correct:             3689 :  98.09%
    4. False-positive:         4 :   0.11%
    5. False-negative:         9 :   0.24%
    6. Missed-ham:            19 :   0.51%
    7. Missed-spam:           40 :   1.06%