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updating the copyright and adding tests
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‎src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachine.cs

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//------------------------------------------------------------------------------
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// <copyright company="Microsoft Corporation">
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// </copyright>
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//------------------------------------------------------------------------------
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// Licensed to the .NET Foundation under one or more agreements.
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// The .NET Foundation licenses this file to you under the MIT license.
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// See the LICENSE file in the project root for more information.
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using System;
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using System.Collections.Generic;

‎src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineInterface.cs

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using Microsoft.ML.Runtime.Internal.CpuMath;
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// Licensed to the .NET Foundation under one or more agreements.
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// The .NET Foundation licenses this file to you under the MIT license.
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// See the LICENSE file in the project root for more information.
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using Microsoft.ML.Runtime.Internal.CpuMath;
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using Microsoft.ML.Runtime.Internal.Utilities;
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using System.Runtime.InteropServices;
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‎src/Microsoft.ML.StandardLearners/Microsoft.ML.StandardLearners.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>

‎test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-out.txt

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maml.exe CV tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures threads=- norm=No dout=%Output% data=%Data% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures}
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Not adding a normalizer.
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Warning: Skipped 8 examples with bad label/weight/features in training set
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Not training a calibrator because it is not needed.
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Not adding a normalizer.
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Warning: Skipped 8 examples with bad label/weight/features in training set
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Not training a calibrator because it is not needed.
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Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
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TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
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Confusion table
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||======================
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PREDICTED || positive | negative | Recall
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TRUTH ||======================
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positive || 122 | 12 | 0.9104
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negative || 4 | 216 | 0.9818
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||======================
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Precision || 0.9683 | 0.9474 |
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OVERALL 0/1 ACCURACY: 0.954802
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LOG LOSS/instance: 0.259660
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Test-set entropy (prior Log-Loss/instance): 0.956998
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LOG-LOSS REDUCTION (RIG): 72.867233
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AUC: 0.984973
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Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
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TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
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Confusion table
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||======================
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PREDICTED || positive | negative | Recall
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TRUTH ||======================
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positive || 92 | 13 | 0.8762
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negative || 2 | 222 | 0.9911
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||======================
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Precision || 0.9787 | 0.9447 |
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OVERALL 0/1 ACCURACY: 0.954407
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LOG LOSS/instance: 0.260480
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Test-set entropy (prior Log-Loss/instance): 0.903454
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LOG-LOSS REDUCTION (RIG): 71.168362
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AUC: 0.967049
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OVERALL RESULTS
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---------------------------------------
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AUC: 0.976011 (0.0090)
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Accuracy: 0.954605 (0.0002)
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Positive precision: 0.973489 (0.0052)
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Positive recall: 0.893319 (0.0171)
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Negative precision: 0.946025 (0.0013)
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Negative recall: 0.986445 (0.0046)
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Log-loss: 0.260070 (0.0004)
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Log-loss reduction: 72.017798 (0.8494)
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F1 Score: 0.931542 (0.0069)
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AUPRC: 0.974115 (0.0054)
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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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--- Progress log ---
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[1] 'Normalize' started.
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[1] (%Time%) 337 examples
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[1] 'Normalize' finished in %Time%.
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[2] 'Training' started.
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[2] (%Time%) 1 iterations, 329 examples Training-loss: 0.371414389819699
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[2] (%Time%) 2 iterations, 329 examples Training-loss: 0.225137821503565
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[2] (%Time%) 3 iterations, 329 examples Training-loss: 0.197323119398265
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[2] (%Time%) 4 iterations, 329 examples Training-loss: 0.183649426646222
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[2] (%Time%) 5 iterations, 329 examples Training-loss: 0.174400635825405
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[2] 'Training' finished in %Time%.
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[3] 'Normalize #2' started.
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[3] (%Time%) 362 examples
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[3] 'Normalize #2' finished in %Time%.
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[4] 'Training #2' started.
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[4] (%Time%) 1 iterations, 354 examples Training-loss: 0.35872800705401
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[4] (%Time%) 2 iterations, 354 examples Training-loss: 0.239609312114266
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[4] (%Time%) 3 iterations, 354 examples Training-loss: 0.210775498912242
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[4] (%Time%) 4 iterations, 354 examples Training-loss: 0.19625903089058
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[4] (%Time%) 5 iterations, 354 examples Training-loss: 0.187121580244397
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[4] 'Training #2' finished in %Time%.

‎test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-rp.txt

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FieldAwareFactorizationMachine
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AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /d /norm /shuf Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
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0.976011 0.954605 0.973489 0.893319 0.946025 0.986445 0.26007 72.0178 0.931542 0.974115 5 - - FieldAwareFactorizationMachine %Data% %Output% 99 0 0 maml.exe CV tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures threads=- norm=No dout=%Output% data=%Data% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} /d:5;/norm:-;/shuf:-
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