Open MichaelChirico opened 6 years ago
Right now, when we optimize a decent number of parameters, the verbose output tends to overrun the allotted options('width') leading to harder-to-ingest clutter like this:
verbose
options('width')
elapsed = 401.33 Round = 1 learning_rate = 0.2420 l1 = 2.7456 l2 = 2.9535 colp = 0.7971 maxd = 3.0000 recent_n = 2.0000 Value = 0.9023 elapsed = 541.09 Round = 2 learning_rate = 0.2142 l1 = 2.1505 l2 = 1.1346 colp = 0.9648 maxd = 6.0000 recent_n = 6.0000 Value = 0.9216 elapsed = 440.59 Round = 3 learning_rate = 0.2231 l1 = 0.9104 l2 = 2.3175 colp = 0.5518 maxd = 4.0000 recent_n = 20.0000 Value = 0.9095 elapsed = 491.44 Round = 4 learning_rate = 0.2762 l1 = 3.6231 l2 = 2.1818 colp = 0.2645 maxd = 5.0000 recent_n = 9.0000 Value = 0.9207 elapsed = 391.57 Round = 5 learning_rate = 0.4966 l1 = 3.9548 l2 = 3.8570 colp = 0.3406 maxd = 3.0000 recent_n = 1.0000 Value = 0.8996 elapsed = 605.43 Round = 6 learning_rate = 0.3980 l1 = 3.3862 l2 = 2.7964 colp = 0.3148 maxd = 7.0000 recent_n = 4.0000 Value = 0.9224 elapsed = 491.11 Round = 7 learning_rate = 0.3946 l1 = 3.8995 l2 = 0.1118 colp = 0.9037 maxd = 5.0000 recent_n = 17.0000 Value = 0.9211 elapsed = 395.37 Round = 8 learning_rate = 0.2047 l1 = 4.0554 l2 = 0.6690 colp = 0.4470 maxd = 3.0000 recent_n = 18.0000 Value = 0.8969 elapsed = 658.94 Round = 9 learning_rate = 0.2818 l1 = 4.6576 l2 = 0.9454 colp = 0.3905 maxd = 8.0000 recent_n = 14.0000 Value = 0.9244 elapsed = 653.89 Round = 10 learning_rate = 0.2750 l1 = 3.2765 l2 = 1.4478 colp = 0.0808 maxd = 8.0000 recent_n = 18.0000 Value = 0.9233
This can be fixed by messing with options('width'), but better seems to me would be to eliminate all the duplicate information like so:
elapse Round learning_rate l1 l2 colp maxd recent_n Value 401.33 1 0.2420 2.7456 2.9535 0.7971 3.0000 2.0000 0.9023 541.09 2 0.2142 2.1505 1.1346 0.9648 6.0000 6.0000 0.9216 440.59 3 0.2231 0.9104 2.3175 0.5518 4.0000 20.0000 0.9095 491.44 4 0.2762 3.6231 2.1818 0.2645 5.0000 9.0000 0.9207 391.57 5 0.4966 3.9548 3.8570 0.3406 3.0000 1.0000 0.8996 605.43 6 0.3980 3.3862 2.7964 0.3148 7.0000 4.0000 0.9224 491.11 7 0.3946 3.8995 0.1118 0.9037 5.0000 17.0000 0.9211 395.37 8 0.2047 4.0554 0.6690 0.4470 3.0000 18.0000 0.8969 658.94 9 0.2818 4.6576 0.9454 0.3905 8.0000 14.0000 0.9244 653.89 10 0.2750 3.2765 1.4478 0.0808 8.0000 18.0000 0.9233
Next, eliminate the spurious zeroes from known integer input:
integer
elapse Round learning_rate l1 l2 colp maxd recent_n Value 401.33 1 0.2420 2.7456 2.9535 0.7971 3 2 0.9023 541.09 2 0.2142 2.1505 1.1346 0.9648 6 6 0.9216 440.59 3 0.2231 0.9104 2.3175 0.5518 4 20 0.9095 491.44 4 0.2762 3.6231 2.1818 0.2645 5 9 0.9207 391.57 5 0.4966 3.9548 3.8570 0.3406 3 1 0.8996 605.43 6 0.3980 3.3862 2.7964 0.3148 7 4 0.9224 491.11 7 0.3946 3.8995 0.1118 0.9037 5 17 0.9211 395.37 8 0.2047 4.0554 0.6690 0.4470 3 18 0.8969 658.94 9 0.2818 4.6576 0.9454 0.3905 8 14 0.9244 653.89 10 0.2750 3.2765 1.4478 0.0808 8 18 0.9233
By my count this is a 40% character reduction in addition to being much more readable.
This is related to #25
Right now, when we optimize a decent number of parameters, the
verboseoutput tends to overrun the allottedoptions('width')leading to harder-to-ingest clutter like this:This can be fixed by messing with
options('width'), but better seems to me would be to eliminate all the duplicate information like so:Next, eliminate the spurious zeroes from known
integerinput:By my count this is a 40% character reduction in addition to being much more readable.
This is related to #25