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fix: stash and restore prediction when calling learn during learn_returns_prediction==false calls #4632

Merged
merged 12 commits into from
Aug 31, 2023
114 changes: 57 additions & 57 deletions test/pred-sets/ref/cats.predict
Original file line number Diff line number Diff line change
@@ -1,57 +1,57 @@
17118.012,0.00016043648
15724.644,0.00016043648
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6641.8545,2.1031378e-06
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22992.766,2.1031378e-06
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22556.402,2.1031378e-06
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20 changes: 10 additions & 10 deletions test/pred-sets/ref/cats_load.predict
Original file line number Diff line number Diff line change
@@ -1,10 +1,10 @@
2.3137953,0.40625
1.8475317,0.40625
2.1299162,0.40625
1.7430687,0.40625
14.327164,0.00625
1.7164373,0.40625
1.748255,0.40625
30.00431,0.00625
0.51225615,0.40625
2.3816023,0.40625
25.271912,0.00625
2.1290207,0.40625
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@ataymano ataymano Aug 22, 2023

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numbers here are crazily different - maybe this test can be reconsidered (in next PRs)

4.473629,0.00625
18.205719,0.00625
2.4059067,0.40625
9.585819,0.00625
0.93930376,0.40625
1.7801242,0.40625
2.0926502,0.40625
0.8038121,0.40625
200 changes: 100 additions & 100 deletions test/pred-sets/ref/cats_room_temp.predict
Original file line number Diff line number Diff line change
@@ -1,100 +1,100 @@
84.607056,0.005
3.9181187,0.08833334
39.260834,0.005
17.99776,0.08833334
39.331844,0.005
6.7627044,0.005
77.12543,0.005
69.8972,0.005
17.184662,0.08833334
54.76903,0.005
0.66596967,0.005
13.026539,0.005
52.522453,0.08833334
86.18144,0.005
49.648746,0.08833334
53.096367,0.08833334
41.92059,0.005
88.03693,0.005
50.15336,0.08833334
1.6463974,0.005
57.090523,0.08833334
25.416866,0.005
94.471146,0.005
43.387676,0.005
59.151047,0.08833334
38.3481,0.005
51.486103,0.08833334
50.88297,0.08833334
85.89383,0.005
55.38167,0.005
51.260296,0.08833334
85.17497,0.005
50.680935,0.08833334
49.601463,0.08833334
59.61712,0.005
76.559,0.005
51.651062,0.08833334
62.96363,0.005
7.8253226,0.005
32.741436,0.005
21.816528,0.005
53.33639,0.08833334
33.895855,0.005
48.782,0.08833334
48.93803,0.08833334
48.645847,0.08833334
53.199036,0.08833334
77.73645,0.005
62.783302,0.005
18.494978,0.005
66.79538,0.005
53.29303,0.08833334
53.5938,0.08833334
47.533524,0.005
8.389192,0.005
28.030102,0.005
52.605915,0.08833334
53.525967,0.08833334
50.158722,0.08833334
47.26561,0.005
72.41367,0.005
1.1448574,0.005
53.4234,0.08833334
88.777985,0.005
95.276276,0.005
49.196915,0.08833334
48.705658,0.08833334
51.38816,0.08833334
9.958978,0.005
95.176796,0.005
56.863132,0.005
48.599724,0.08833334
42.992935,0.005
49.75944,0.08833334
21.298155,0.005
21.954062,0.005
54.1602,0.08833334
53.210964,0.08833334
52.06177,0.08833334
55.077793,0.005
50.11463,0.08833334
55.921093,0.005
67.825,0.005
74.41847,0.005
79.864365,0.005
24.734612,0.005
53.9586,0.08833334
94.53804,0.005
48.65285,0.08833334
67.55201,0.005
48.724384,0.08833334
50.91952,0.08833334
40.403355,0.005
46.697994,0.005
49.63687,0.08833334
50.199287,0.08833334
52.23696,0.08833334
48.77658,0.08833334
52.563095,0.08833334
14.676942,0.005
53.204613,0.08833334
9.196535,0.005
17.174236,0.08833334
77.29088,0.005
15.437802,0.08833334
93.60582,0.005
73.45269,0.005
19.235268,0.08833334
40.03132,0.005
18.64663,0.08833334
66.666214,0.005
53.193462,0.08833334
34.754963,0.005
34.70198,0.005
57.403484,0.005
67.80745,0.005
86.91858,0.005
52.78149,0.08833334
43.365547,0.005
55.016094,0.08833334
8.145193,0.005
57.61373,0.08833334
50.82185,0.08833334
48.282825,0.005
81.51001,0.005
26.397629,0.005
23.397736,0.005
50.563953,0.08833334
53.253433,0.08833334
53.1661,0.08833334
50.818462,0.08833334
72.26961,0.005
73.63446,0.005
70.71592,0.005
23.83146,0.005
51.322205,0.08833334
26.74537,0.005
54.481422,0.08833334
52.270885,0.08833334
48.716354,0.08833334
9.419978,0.005
2.7159307,0.005
51.904087,0.08833334
51.104538,0.08833334
0.30527782,0.005
76.84366,0.005
20.968102,0.005
58.275642,0.005
6.868239,0.005
40.830856,0.005
54.12306,0.08833334
51.63766,0.08833334
95.59329,0.005
48.718655,0.08833334
53.290802,0.08833334
80.52641,0.005
50.78907,0.08833334
12.805883,0.005
89.139755,0.005
76.673355,0.005
55.5548,0.005
11.593977,0.005
51.98833,0.08833334
30.242346,0.005
66.171425,0.005
51.027653,0.08833334
53.797455,0.08833334
91.31642,0.005
75.98573,0.005
51.8337,0.08833334
95.297424,0.005
54.301224,0.08833334
49.036102,0.08833334
31.043774,0.005
56.56052,0.005
48.77767,0.08833334
53.00767,0.08833334
49.76527,0.08833334
28.06632,0.005
53.554474,0.08833334
54.24915,0.08833334
15.149416,0.005
48.76757,0.08833334
48.71854,0.08833334
52.769234,0.08833334
79.30612,0.005
2.7464993,0.005
53.082054,0.08833334
58.80374,0.005
49.790363,0.08833334
56.0045,0.005
88.90586,0.005
52.904503,0.08833334
52.2649,0.08833334
11.048922,0.005
53.87539,0.08833334
49.905197,0.08833334
42.230206,0.005
42.714077,0.005
68.1033,0.005
10 changes: 5 additions & 5 deletions test/pred-sets/ref/cats_save.predict
Original file line number Diff line number Diff line change
@@ -1,10 +1,10 @@
0.0012017498,0.00625
0.6113522,0.40625
0.5820448,0.40625
2.2343192,0.40625
20.461426,0.00625
1.2286053,0.40625
10.350056,0.00625
22.592487,0.00625
2.2877245,0.40625
1.3163464,0.40625
2.1290207,0.40625
18.205719,0.00625
9.585819,0.00625
1.7801242,0.40625
0.8038121,0.40625
12 changes: 6 additions & 6 deletions test/train-sets/ref/0001-replay.stderr
Original file line number Diff line number Diff line change
Expand Up @@ -18,18 +18,18 @@ loss last counter weight label predict feat
0.250000 0.250000 8 8.0 0.0000 0.0000 860
0.312500 0.375000 16 16.0 1.0000 0.0000 128
0.343750 0.375000 32 32.0 0.0000 0.0000 176
0.350946 0.358141 64 64.0 0.0000 0.0275 350
0.371181 0.391417 128 128.0 1.0000 0.1510 620
0.292717 0.214252 256 256.0 0.0000 0.3660 410
0.149202 0.005688 512 512.0 0.0000 0.0000 278
0.074603 0.000004 1024 1024.0 1.0000 1.0000 170
0.359375 0.375000 64 64.0 0.0000 0.0275 350
0.414062 0.468750 128 128.0 1.0000 0.1510 620
0.433594 0.453125 256 256.0 0.0000 0.3660 410
0.441406 0.449219 512 512.0 0.0000 0.0000 278
0.453125 0.464844 1024 1024.0 1.0000 1.0000 170

finished run
number of examples per pass = 200
passes used = 8
weighted example sum = 1600.000000
weighted label sum = 728.000000
average loss = 0.047746
average loss = 0.455000
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so loss went up?

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Previous loss was cheating since it was using predictions that were coming out of learn.

best constant = 0.455000
best constant's loss = 0.247975
total feature number = 717536
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