Updated AveragedPerceptron default iterations from 1 to 10 (#5258)

* updated averaged perceptron default tries from 1 to 10

* Update CmdParser.cs

* Update CmdParser.cs

* reverted changes to CmdParser.cs

* reverted changes to CmdParser.cs

Author: michaelgsharp

src/Microsoft.ML.StandardTrainers/Standard/Online/AveragedPerceptron.cs

@@ -82,6 +82,11 @@ public sealed class AveragedPerceptronTrainer : AveragedLinearTrainer<BinaryPred
         /// </summary>
         public sealed class Options : AveragedLinearOptions
         {
+            public Options()
+            {
+                NumberOfIterations = 10;
+            }
+
             /// <summary>
             /// A custom <a href="https://en.wikipedia.org/wiki/Loss_function">loss</a>.
             /// </summary>

test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt

@@ -1,10 +1,10 @@
 maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1
 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
-Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)
+Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
 Training calibrator.
-PAV calibrator:  piecewise function approximation has 6 components.
+PAV calibrator:  piecewise function approximation has 5 components.
 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
-Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)
+Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
 Training calibrator.
 PAV calibrator:  piecewise function approximation has 6 components.
 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
@@ -13,43 +13,43 @@ Confusion table
           ||======================
 PREDICTED || positive | negative | Recall
 TRUTH     ||======================
- positive ||      132 |        2 | 0.9851
+ positive ||      133 |        1 | 0.9925
  negative ||        9 |      211 | 0.9591
           ||======================
-Precision ||   0.9362 |   0.9906 |
-OVERALL 0/1 ACCURACY: 0.968927
+Precision ||   0.9366 |   0.9953 |
+OVERALL 0/1 ACCURACY: 0.971751
 LOG LOSS/instance:  Infinity
 Test-set entropy (prior Log-Loss/instance): 0.956998
 LOG-LOSS REDUCTION (RIG): -Infinity
-AUC:                0.992809
+AUC:                0.994403
 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
 TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
 Confusion table
           ||======================
 PREDICTED || positive | negative | Recall
 TRUTH     ||======================
- positive ||      102 |        3 | 0.9714
- negative ||        4 |      220 | 0.9821
+ positive ||      100 |        5 | 0.9524
+ negative ||        3 |      221 | 0.9866
           ||======================
-Precision ||   0.9623 |   0.9865 |
-OVERALL 0/1 ACCURACY: 0.978723
-LOG LOSS/instance:  0.239330
+Precision ||   0.9709 |   0.9779 |
+OVERALL 0/1 ACCURACY: 0.975684
+LOG LOSS/instance:  0.227705
 Test-set entropy (prior Log-Loss/instance): 0.903454
-LOG-LOSS REDUCTION (RIG): 0.735095
-AUC:                0.997279
+LOG-LOSS REDUCTION (RIG): 0.747961
+AUC:                0.997619
 
 OVERALL RESULTS
 ---------------------------------------
-AUC:                0.995044 (0.0022)
-Accuracy:           0.973825 (0.0049)
-Positive precision: 0.949217 (0.0130)
-Positive recall:    0.978252 (0.0068)
-Negative precision: 0.988579 (0.0020)
-Negative recall:    0.970617 (0.0115)
+AUC:                0.996011 (0.0016)
+Accuracy:           0.973718 (0.0020)
+Positive precision: 0.953747 (0.0171)
+Positive recall:    0.972459 (0.0201)
+Negative precision: 0.986580 (0.0087)
+Negative recall:    0.972849 (0.0138)
 Log-loss:           Infinity (NaN)
 Log-loss reduction: -Infinity (NaN)
-F1 Score:           0.963412 (0.0034)
-AUPRC:              0.990172 (0.0037)
+F1 Score:           0.962653 (0.0011)
+AUPRC:              0.992269 (0.0025)
 
 ---------------------------------------
 Physical memory usage(MB): %Number%

test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt

@@ -1,4 +1,4 @@
 AveragedPerceptron
 AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
-0.995044	0.973825	0.949217	0.978252	0.988579	0.970617	Infinity	-Infinity	0.963412	0.990172	AveragedPerceptron	%Data%		%Output%	99	0	0	maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1		
+0.996011	0.973718	0.953747	0.972459	0.98658	0.972849	Infinity	-Infinity	0.962653	0.992269	AveragedPerceptron	%Data%		%Output%	99	0	0	maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1