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Calibrator trainer needs to clear data so it can train another calibrator with new data (#1805)
1 parent 05284d6 commit 0678df5

21 files changed

+1326
-1
lines changed

‎src/Microsoft.ML.Data/Prediction/Calibrator.cs

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1112,7 +1112,9 @@ public bool ProcessTrainingExample(Float output, bool labelIs1, Float weight)
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public ICalibrator FinishTraining(IChannel ch)
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{
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ch.Check(Data != null, "Calibrator trained on zero instances.");
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return CreateCalibrator(ch);
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var calibrator = CreateCalibrator(ch);
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Data = null;
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return calibrator;
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}
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public abstract ICalibrator CreateCalibrator(IChannel ch);

‎test/BaselineOutput/Common/OVA/OVA-CV-iris-out.txt

Lines changed: 58 additions & 0 deletions
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maml.exe CV tr=OVA{p=AvgPer{ lr=0.8 }} threads=- norm=No dout=%Output% data=%Data% seed=1
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Not adding a normalizer.
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Training learner 0
4+
Training calibrator.
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Training learner 1
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Training calibrator.
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Training learner 2
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Training calibrator.
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Not training a calibrator because it is not needed.
10+
Not adding a normalizer.
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Training learner 0
12+
Training calibrator.
13+
Training learner 1
14+
Training calibrator.
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Training learner 2
16+
Training calibrator.
17+
Not training a calibrator because it is not needed.
18+
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Confusion table
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||========================
21+
PREDICTED || 0 | 1 | 2 | Recall
22+
TRUTH ||========================
23+
0 || 21 | 0 | 0 | 1.0000
24+
1 || 0 | 22 | 8 | 0.7333
25+
2 || 0 | 0 | 28 | 1.0000
26+
||========================
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Precision ||1.0000 |1.0000 |0.7778 |
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Accuracy(micro-avg): 0.898734
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Accuracy(macro-avg): 0.911111
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Log-loss: 0.372620
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Log-loss reduction: 65.736556
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Confusion table
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||========================
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PREDICTED || 0 | 1 | 2 | Recall
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TRUTH ||========================
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0 || 29 | 0 | 0 | 1.0000
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1 || 0 | 18 | 2 | 0.9000
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2 || 0 | 0 | 22 | 1.0000
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||========================
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Precision ||1.0000 |1.0000 |0.9167 |
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Accuracy(micro-avg): 0.971831
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Accuracy(macro-avg): 0.966667
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Log-loss: 0.357704
45+
Log-loss reduction: 67.051654
46+
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OVERALL RESULTS
48+
---------------------------------------
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Accuracy(micro-avg): 0.935283 (0.0365)
50+
Accuracy(macro-avg): 0.938889 (0.0278)
51+
Log-loss: 0.365162 (0.0075)
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Log-loss reduction: 66.394105 (0.6575)
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---------------------------------------
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Physical memory usage(MB): %Number%
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Virtual memory usage(MB): %Number%
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%DateTime% Time elapsed(s): %Number%
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‎test/BaselineOutput/Common/OVA/OVA-CV-iris-rp.txt

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OVA
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Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /p Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
3+
0.935283 0.938889 0.365162 66.3941 AvgPer{lr=0.8} OVA %Data% %Output% 99 0 0 maml.exe CV tr=OVA{p=AvgPer{ lr=0.8 }} threads=- norm=No dout=%Output% data=%Data% seed=1 /p:AvgPer{lr=0.8}
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‎test/BaselineOutput/Common/OVA/OVA-CV-iris.txt

Lines changed: 151 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,151 @@
1+
Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
2+
5 0 0 0.043587643807703136 0.957348645 0.04264102 1.03425764E-05 0 1 2
3+
6 0 0 0.20844569128859777 0.8118451 0.188126311 2.8563165E-05 0 1 2
4+
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5+
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6+
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7+
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8+
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9+
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10+
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11+
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12+
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13+
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14+
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15+
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‎test/BaselineOutput/Common/OVA/OVA-FastForest-CV-iris-out.txt

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maml.exe CV tr=OVA{p=FastForest{ }} threads=- norm=No dout=%Output% data=%Data% seed=1
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Not adding a normalizer.
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Training learner 0
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Making per-feature arrays
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Changing data from row-wise to column-wise
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Processed 71 instances
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Binning and forming Feature objects
8+
Reserved memory for tree learner: %Number% bytes
9+
Starting to train ...
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Warning: 2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
11+
Training calibrator.
12+
Training learner 1
13+
Making per-feature arrays
14+
Changing data from row-wise to column-wise
15+
Processed 71 instances
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Binning and forming Feature objects
17+
Reserved memory for tree learner: %Number% bytes
18+
Starting to train ...
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Warning: 3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
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Training calibrator.
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Training learner 2
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Making per-feature arrays
23+
Changing data from row-wise to column-wise
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Processed 71 instances
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Binning and forming Feature objects
26+
Reserved memory for tree learner: %Number% bytes
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Starting to train ...
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Warning: 1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
29+
Training calibrator.
30+
Not training a calibrator because it is not needed.
31+
Not adding a normalizer.
32+
Training learner 0
33+
Making per-feature arrays
34+
Changing data from row-wise to column-wise
35+
Processed 79 instances
36+
Binning and forming Feature objects
37+
Reserved memory for tree learner: %Number% bytes
38+
Starting to train ...
39+
Warning: 2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
40+
Training calibrator.
41+
Training learner 1
42+
Making per-feature arrays
43+
Changing data from row-wise to column-wise
44+
Processed 79 instances
45+
Binning and forming Feature objects
46+
Reserved memory for tree learner: %Number% bytes
47+
Starting to train ...
48+
Warning: 3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
49+
Training calibrator.
50+
Training learner 2
51+
Making per-feature arrays
52+
Changing data from row-wise to column-wise
53+
Processed 79 instances
54+
Binning and forming Feature objects
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Reserved memory for tree learner: %Number% bytes
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Starting to train ...
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Warning: 1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
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Training calibrator.
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Not training a calibrator because it is not needed.
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Confusion table
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||========================
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PREDICTED || 0 | 1 | 2 | Recall
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TRUTH ||========================
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0 || 21 | 0 | 0 | 1.0000
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1 || 0 | 25 | 5 | 0.8333
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2 || 0 | 1 | 27 | 0.9643
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||========================
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Precision ||1.0000 |0.9615 |0.8438 |
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Accuracy(micro-avg): 0.924051
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Accuracy(macro-avg): 0.932540
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Log-loss: 0.197783
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Log-loss reduction: 81.813342
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Confusion table
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||========================
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PREDICTED || 0 | 1 | 2 | Recall
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TRUTH ||========================
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0 || 29 | 0 | 0 | 1.0000
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1 || 0 | 19 | 1 | 0.9500
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2 || 0 | 2 | 20 | 0.9091
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||========================
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Precision ||1.0000 |0.9048 |0.9524 |
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Accuracy(micro-avg): 0.957746
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Accuracy(macro-avg): 0.953030
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Log-loss: 0.103360
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Log-loss reduction: 90.479422
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OVERALL RESULTS
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---------------------------------------
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Accuracy(micro-avg): 0.940899 (0.0168)
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Accuracy(macro-avg): 0.942785 (0.0102)
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Log-loss: 0.150571 (0.0472)
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Log-loss reduction: 86.146382 (4.3330)
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---------------------------------------
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Physical memory usage(MB): %Number%
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Virtual memory usage(MB): %Number%
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%DateTime% Time elapsed(s): %Number%
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‎test/BaselineOutput/Common/OVA/OVA-FastForest-CV-iris-rp.txt

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OVA
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Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /p Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
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0.940899 0.942785 0.150571 86.14639 FastForest{} OVA %Data% %Output% 99 0 0 maml.exe CV tr=OVA{p=FastForest{ }} threads=- norm=No dout=%Output% data=%Data% seed=1 /p:FastForest{}
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