From 651ba59f95348fe52f53e6ddeb8000df37cfdbf3 Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Mon, 25 Sep 2017 10:20:14 -0400
Subject: [PATCH] Re-using random search for late weighted linear, added Readme
---
Code/MonoMutliViewClassifiers/ExecClassif.py | 17 -
.../Monoview/ExecClassifMonoView.py | 3 -
.../MonoviewClassifiers/Adaboost.py | 2 +-
.../EarlyFusionPackage/WeightedLinear.py | 2 +-
Code/MonoMutliViewClassifiers/Readme.md | 88 ++
...-cq-hist_lss-hist_phog-hist-awaexp-LOG.log | 1266 +++++++++++++++++
6 files changed, 1356 insertions(+), 22 deletions(-)
create mode 100644 Code/MonoMutliViewClassifiers/Readme.md
diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index f16a1e46..463439d6 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -330,23 +330,6 @@ def initMultiviewArguments(args, benchmark, views, viewsIndices, accuracies, cla
return argumentDictionaries
-# def analyzeLabels(labelsArrays, realLabels, classifiersNames):
-# nbClassifiers = len(classifiersNames)
-# nbExamples = realLabels.shape[0]
-# nbIter = nbExamples/nbClassifiers
-# data = np.zeros((nbExamples, nbClassifiers*nbIter))
-# tempData = np.array([labelsArray == realLabels for labelsArray in np.transpose(labelsArrays)]).astype(int)
-# for classifierIndex in range(nbClassifiers):
-# for iterIndex in range(nbIter):
-# data[:,classifierIndex*nbIter+iterIndex] = tempData[classifierIndex,:]
-# fig, ax = plt.subplots()
-# cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
-# ax.set_title('Error on examples depending on the classifier')
-# cbar = fig.colorbar(cax, ticks=[0, 1])
-# cbar.ax.set_yticklabels(['Wrong', ' Right'])
-# fig.savefig("Results/"+time.strftime("%Y%m%d-%H%M%S")+"error_analysis.png")
-
-
parser = argparse.ArgumentParser(
description='This file is used to benchmark the accuracies fo multiple classification algorithm on multiview data.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 327974a8..214b34c3 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -68,11 +68,8 @@ def ExecMonoview(X, Y, name, labelsNames, learningRate, nbFolds, nbCores, databa
# Calculate Train/Test data
logging.debug("Start:\t Determine Train/Test split"+" for iteration "+str(iterationStat+1))
testIndices = ClassifMonoView.splitDataset(Y, nbClass, learningRate, datasetLength)
- print "fromage"
trainIndices = [i for i in range(datasetLength) if i not in testIndices]
- print "jqmbon"
X_train = extractSubset(X,trainIndices)
- print "poulet"
X_test = extractSubset(X,testIndices)
y_train = Y[trainIndices]
y_test = Y[testIndices]
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
index e1d2e0de..4e9b1c76 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
@@ -33,7 +33,7 @@ def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIt
else:
metricKWARGS = {}
scorer = metricModule.get_scorer(**metricKWARGS)
- grid = RandomizedSearchCV(pipeline, n_iter=nIter, param_distributions=param,refit=True,n_jobs=nbCores,scoring=scorer,cv=nbFolds)
+ grid = RandomizedSearchCV(pipeline, n_iter=nIter, param_distributions=param, refit=True, n_jobs=nbCores, scoring=scorer, cv=nbFolds)
detector = grid.fit(X_train, y_train)
desc_estimators = [detector.best_params_["classifier__n_estimators"],
detector.best_params_["classifier__base_estimator"]]
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
index 0f49fc3b..97c0ac4a 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
@@ -32,7 +32,7 @@ def gridSearch(DATASET, classificationKWARGS, trainIndices, nIter=30, viewsIndic
if accuracy > bestScore:
bestScore = accuracy
bestConfig = normalizedArray
- return [np.array([1.0 for i in range(nbView)])]
+ return [bestConfig]
class WeightedLinear(EarlyFusionClassifier):
diff --git a/Code/MonoMutliViewClassifiers/Readme.md b/Code/MonoMutliViewClassifiers/Readme.md
new file mode 100644
index 00000000..cbe3fcd9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Readme.md
@@ -0,0 +1,88 @@
+# Project Title
+
+One Paragraph of project description goes here
+
+## Getting Started
+
+These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
+
+### Prerequisites
+
+What things you need to install the software and how to install them
+
+```
+Give examples
+```
+
+### Installing
+
+A step by step series of examples that tell you have to get a development env running
+
+Say what the step will be
+
+```
+Give the example
+```
+
+And repeat
+
+```
+until finished
+```
+
+End with an example of getting some data out of the system or using it for a little demo
+
+## Running the tests
+
+Explain how to run the automated tests for this system
+
+### Break down into end to end tests
+
+Explain what these tests test and why
+
+```
+Give an example
+```
+
+### And coding style tests
+
+Explain what these tests test and why
+
+```
+Give an example
+```
+
+## Deployment
+
+Add additional notes about how to deploy this on a live system
+
+## Built With
+
+* [Dropwizard](http://www.dropwizard.io/1.0.2/docs/) - The web framework used
+* [Maven](https://maven.apache.org/) - Dependency Management
+* [ROME](https://rometools.github.io/rome/) - Used to generate RSS Feeds
+
+## Contributing
+
+Please read [CONTRIBUTING.md](https://gist.github.com/PurpleBooth/b24679402957c63ec426) for details on our code of conduct, and the process for submitting pull requests to us.
+
+## Versioning
+
+We use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/your/project/tags).
+
+## Authors
+
+* **Billie Thompson** - *Initial work* - [PurpleBooth](https://github.com/PurpleBooth)
+
+See also the list of [contributors](https://github.com/your/project/contributors) who participated in this project.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
+
+## Acknowledgments
+
+* Hat tip to anyone who's code was used
+* Inspiration
+* etc
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154253-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-154253-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log
index b724a44a..bbc56280 100644
--- a/Code/MonoMutliViewClassifiers/Results/20170922-154253-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154253-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log
@@ -706,3 +706,1269 @@ Classifier configuration :
2017-09-22 15:50:07,562 DEBUG: Done: Determine Train/Test split
2017-09-22 15:50:07,562 DEBUG: Start: RandomSearch best settings with 20 iterations for RandomForest
2017-09-22 15:50:07,562 DEBUG: Start: RandomSearch best settings with 20 iterations for KNN
+2017-09-22 15:50:24,063 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:50:24,063 DEBUG: Start: Training
+2017-09-22 15:50:24,385 DEBUG: Done: Training
+2017-09-22 15:50:24,385 DEBUG: Start: Predicting
+2017-09-22 15:50:24,467 DEBUG: Done: Predicting
+2017-09-22 15:50:24,467 DEBUG: Info: Time for training and predicting: 16.9445199966[s]
+2017-09-22 15:50:24,467 DEBUG: Start: Getting Results
+2017-09-22 15:50:24,495 DEBUG: Done: Getting Results
+2017-09-22 15:50:24,495 INFO: Classification on awaexp database for lss-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.677914110429, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 28, max_depth : 12
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.677914110429
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.682779456193
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.682779456193
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.322085889571
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.677914110429
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.355995746702
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672619047619
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.677914110429
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.322085889571
+
+
+ Classification took 0:00:16
+2017-09-22 15:50:24,496 INFO: Done: Result Analysis
+2017-09-22 15:50:43,708 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:50:43,708 DEBUG: Start: Training
+2017-09-22 15:50:43,739 DEBUG: Done: Training
+2017-09-22 15:50:43,739 DEBUG: Start: Predicting
+2017-09-22 15:50:49,339 DEBUG: Done: Predicting
+2017-09-22 15:50:49,340 DEBUG: Info: Time for training and predicting: 41.8171880245[s]
+2017-09-22 15:50:49,340 DEBUG: Start: Getting Results
+2017-09-22 15:50:49,368 DEBUG: Done: Getting Results
+2017-09-22 15:50:49,369 INFO: Classification on awaexp database for lss-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.711488250653, with STD : 0.0
+accuracy_score on test : 0.59509202454, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 21
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.711488250653
+ - Score on test : 0.59509202454
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.675477239354
+ - Score on test : 0.551020408163
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.675477239354
+ - Score on test : 0.551020408163
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.288511749347
+ - Score on test : 0.40490797546
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.711488250653
+ - Score on test : 0.59509202454
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.433794414692
+ - Score on test : 0.19395846585
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.771812080537
+ - Score on test : 0.618320610687
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.600522193211
+ - Score on test : 0.496932515337
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.711488250653
+ - Score on test : 0.59509202454
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.288511749347
+ - Score on test : 0.40490797546
+
+
+ Classification took 0:00:41
+2017-09-22 15:50:49,369 INFO: Done: Result Analysis
+2017-09-22 15:50:49,472 DEBUG: Start: Loading data
+2017-09-22 15:50:49,472 DEBUG: Start: Loading data
+2017-09-22 15:50:49,484 DEBUG: Done: Loading data
+2017-09-22 15:50:49,484 DEBUG: Done: Loading data
+2017-09-22 15:50:49,484 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2017-09-22 15:50:49,484 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2017-09-22 15:50:49,485 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:50:49,485 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:50:49,514 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:50:49,514 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:50:49,515 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:50:49,515 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:50:49,515 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:50:49,515 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:50:49,515 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMLinear
+2017-09-22 15:50:49,515 DEBUG: Start: RandomSearch best settings with 20 iterations for SGD
+2017-09-22 15:50:58,244 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:50:58,244 DEBUG: Start: Training
+2017-09-22 15:50:58,287 DEBUG: Done: Training
+2017-09-22 15:50:58,287 DEBUG: Start: Predicting
+2017-09-22 15:50:58,294 DEBUG: Done: Predicting
+2017-09-22 15:50:58,295 DEBUG: Info: Time for training and predicting: 8.82169294357[s]
+2017-09-22 15:50:58,295 DEBUG: Start: Getting Results
+2017-09-22 15:50:58,326 DEBUG: Done: Getting Results
+2017-09-22 15:50:58,326 INFO: Classification on awaexp database for lss-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.955613577023, with STD : 0.0
+accuracy_score on test : 0.791411042945, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.955613577023
+ - Score on test : 0.791411042945
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.956072351421
+ - Score on test : 0.805714285714
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.956072351421
+ - Score on test : 0.805714285714
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0443864229765
+ - Score on test : 0.208588957055
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.955613577023
+ - Score on test : 0.791411042945
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.911426002045
+ - Score on test : 0.589244315989
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.946291560102
+ - Score on test : 0.754010695187
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.865030674847
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.955613577023
+ - Score on test : 0.791411042945
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0443864229765
+ - Score on test : 0.208588957055
+
+
+ Classification took 0:00:08
+2017-09-22 15:50:58,327 INFO: Done: Result Analysis
+2017-09-22 15:51:55,058 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:51:55,058 DEBUG: Start: Training
+2017-09-22 15:51:58,941 DEBUG: Done: Training
+2017-09-22 15:51:58,941 DEBUG: Start: Predicting
+2017-09-22 15:52:00,817 DEBUG: Done: Predicting
+2017-09-22 15:52:00,817 DEBUG: Info: Time for training and predicting: 71.3445019722[s]
+2017-09-22 15:52:00,818 DEBUG: Start: Getting Results
+2017-09-22 15:52:00,845 DEBUG: Done: Getting Results
+2017-09-22 15:52:00,845 INFO: Classification on awaexp database for lss-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.806748466258, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3750
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.806748466258
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809667673716
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809667673716
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.193251533742
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.806748466258
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.613785770175
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.797619047619
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.822085889571
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.806748466258
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.193251533742
+
+
+ Classification took 0:01:11
+2017-09-22 15:52:00,845 INFO: Done: Result Analysis
+2017-09-22 15:52:00,999 DEBUG: Start: Loading data
+2017-09-22 15:52:00,999 DEBUG: Start: Loading data
+2017-09-22 15:52:01,011 DEBUG: Done: Loading data
+2017-09-22 15:52:01,011 DEBUG: Done: Loading data
+2017-09-22 15:52:01,011 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2017-09-22 15:52:01,011 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2017-09-22 15:52:01,011 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:52:01,011 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:52:01,040 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:52:01,040 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:52:01,040 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:52:01,040 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:52:01,041 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:52:01,041 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:52:01,041 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMRBF
+2017-09-22 15:52:01,041 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMPoly
+2017-09-22 15:53:09,464 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:53:09,465 DEBUG: Start: Training
+2017-09-22 15:53:14,572 DEBUG: Done: Training
+2017-09-22 15:53:14,573 DEBUG: Start: Predicting
+2017-09-22 15:53:17,073 DEBUG: Done: Predicting
+2017-09-22 15:53:17,074 DEBUG: Info: Time for training and predicting: 76.0741391182[s]
+2017-09-22 15:53:17,074 DEBUG: Start: Getting Results
+2017-09-22 15:53:17,103 DEBUG: Done: Getting Results
+2017-09-22 15:53:17,104 INFO: Classification on awaexp database for lss-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.800613496933, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Poly with C : 7894
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.790996784566
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.790996784566
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.199386503067
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.603789028059
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.831081081081
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.754601226994
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.199386503067
+
+
+ Classification took 0:01:16
+2017-09-22 15:53:17,104 INFO: Done: Result Analysis
+2017-09-22 15:54:06,512 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:54:06,513 DEBUG: Start: Training
+2017-09-22 15:54:13,323 DEBUG: Done: Training
+2017-09-22 15:54:13,324 DEBUG: Start: Predicting
+2017-09-22 15:54:17,191 DEBUG: Done: Predicting
+2017-09-22 15:54:17,191 DEBUG: Info: Time for training and predicting: 136.191291094[s]
+2017-09-22 15:54:17,191 DEBUG: Start: Getting Results
+2017-09-22 15:54:17,219 DEBUG: Done: Getting Results
+2017-09-22 15:54:17,219 INFO: Classification on awaexp database for lss-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.530674846626, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM RBF with C : 3750
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.673773987207
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.673773987207
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.469325153374
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.127827498141
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.516339869281
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.969325153374
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.469325153374
+
+
+ Classification took 0:02:16
+2017-09-22 15:54:17,219 INFO: Done: Result Analysis
+2017-09-22 15:54:17,347 DEBUG: Start: Loading data
+2017-09-22 15:54:17,347 DEBUG: Start: Loading data
+2017-09-22 15:54:17,447 DEBUG: Done: Loading data
+2017-09-22 15:54:17,447 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2017-09-22 15:54:17,448 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:17,453 DEBUG: Done: Loading data
+2017-09-22 15:54:17,453 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2017-09-22 15:54:17,454 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:17,460 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:17,461 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:17,461 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:17,461 DEBUG: Start: RandomSearch best settings with 20 iterations for DecisionTree
+2017-09-22 15:54:17,466 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:17,466 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:17,467 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:17,467 DEBUG: Start: RandomSearch best settings with 20 iterations for Adaboost
+2017-09-22 15:54:27,870 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:54:27,870 DEBUG: Start: Training
+2017-09-22 15:54:27,990 DEBUG: Done: Training
+2017-09-22 15:54:27,990 DEBUG: Start: Predicting
+2017-09-22 15:54:27,992 DEBUG: Done: Predicting
+2017-09-22 15:54:27,992 DEBUG: Info: Time for training and predicting: 10.644974947[s]
+2017-09-22 15:54:27,992 DEBUG: Start: Getting Results
+2017-09-22 15:54:28,021 DEBUG: Done: Getting Results
+2017-09-22 15:54:28,022 INFO: Classification on awaexp database for phog-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.936031331593, with STD : 0.0
+accuracy_score on test : 0.61963190184, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.61963190184
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.935611038108
+ - Score on test : 0.624242424242
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.935611038108
+ - Score on test : 0.624242424242
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0639686684073
+ - Score on test : 0.38036809816
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.61963190184
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.872136984892
+ - Score on test : 0.239335879234
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.941798941799
+ - Score on test : 0.616766467066
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.631901840491
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.61963190184
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0639686684073
+ - Score on test : 0.38036809816
+
+
+ Classification took 0:00:10
+2017-09-22 15:54:28,022 INFO: Done: Result Analysis
+2017-09-22 15:54:29,890 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:54:29,890 DEBUG: Start: Training
+2017-09-22 15:54:30,042 DEBUG: Done: Training
+2017-09-22 15:54:30,042 DEBUG: Start: Predicting
+2017-09-22 15:54:30,046 DEBUG: Done: Predicting
+2017-09-22 15:54:30,046 DEBUG: Info: Time for training and predicting: 12.6987230778[s]
+2017-09-22 15:54:30,046 DEBUG: Start: Getting Results
+2017-09-22 15:54:30,074 DEBUG: Done: Getting Results
+2017-09-22 15:54:30,074 INFO: Classification on awaexp database for phog-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.592024539877, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 5, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None,
+ min_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.592024539877
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.605341246291
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.605341246291
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.407975460123
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.592024539877
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.184469612979
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.586206896552
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.592024539877
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.407975460123
+
+
+ Classification took 0:00:12
+2017-09-22 15:54:30,075 INFO: Done: Result Analysis
+2017-09-22 15:54:30,216 DEBUG: Start: Loading data
+2017-09-22 15:54:30,217 DEBUG: Start: Loading data
+2017-09-22 15:54:30,219 DEBUG: Done: Loading data
+2017-09-22 15:54:30,219 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2017-09-22 15:54:30,219 DEBUG: Done: Loading data
+2017-09-22 15:54:30,219 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:30,220 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2017-09-22 15:54:30,220 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:30,236 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:30,236 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:30,236 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:30,236 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:30,236 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:30,236 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:30,236 DEBUG: Start: RandomSearch best settings with 20 iterations for KNN
+2017-09-22 15:54:30,236 DEBUG: Start: RandomSearch best settings with 20 iterations for RandomForest
+2017-09-22 15:54:35,705 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:54:35,705 DEBUG: Start: Training
+2017-09-22 15:54:35,710 DEBUG: Done: Training
+2017-09-22 15:54:35,710 DEBUG: Start: Predicting
+2017-09-22 15:54:36,430 DEBUG: Done: Predicting
+2017-09-22 15:54:36,431 DEBUG: Info: Time for training and predicting: 6.21422100067[s]
+2017-09-22 15:54:36,431 DEBUG: Start: Getting Results
+2017-09-22 15:54:36,459 DEBUG: Done: Getting Results
+2017-09-22 15:54:36,460 INFO: Classification on awaexp database for phog-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.648825065274, with STD : 0.0
+accuracy_score on test : 0.58282208589, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.648825065274
+ - Score on test : 0.58282208589
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.601481481481
+ - Score on test : 0.549668874172
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.601481481481
+ - Score on test : 0.549668874172
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.351174934726
+ - Score on test : 0.41717791411
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.648825065274
+ - Score on test : 0.58282208589
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.306425042338
+ - Score on test : 0.167469437176
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.695205479452
+ - Score on test : 0.597122302158
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.530026109661
+ - Score on test : 0.509202453988
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.648825065274
+ - Score on test : 0.58282208589
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.351174934726
+ - Score on test : 0.41717791411
+
+
+ Classification took 0:00:06
+2017-09-22 15:54:36,460 INFO: Done: Result Analysis
+2017-09-22 15:54:44,234 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:54:44,234 DEBUG: Start: Training
+2017-09-22 15:54:44,511 DEBUG: Done: Training
+2017-09-22 15:54:44,511 DEBUG: Start: Predicting
+2017-09-22 15:54:44,584 DEBUG: Done: Predicting
+2017-09-22 15:54:44,585 DEBUG: Info: Time for training and predicting: 14.3676609993[s]
+2017-09-22 15:54:44,585 DEBUG: Start: Getting Results
+2017-09-22 15:54:44,617 DEBUG: Done: Getting Results
+2017-09-22 15:54:44,617 INFO: Classification on awaexp database for phog-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.680981595092, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 28, max_depth : 12
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.680981595092
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.679012345679
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.679012345679
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.319018404908
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.680981595092
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.361990440293
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683229813665
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.674846625767
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.680981595092
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.319018404908
+
+
+ Classification took 0:00:14
+2017-09-22 15:54:44,617 INFO: Done: Result Analysis
+2017-09-22 15:54:44,703 DEBUG: Start: Loading data
+2017-09-22 15:54:44,703 DEBUG: Start: Loading data
+2017-09-22 15:54:44,706 DEBUG: Done: Loading data
+2017-09-22 15:54:44,706 DEBUG: Done: Loading data
+2017-09-22 15:54:44,706 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2017-09-22 15:54:44,706 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2017-09-22 15:54:44,706 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:44,706 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:44,721 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:44,721 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:44,721 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:44,721 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:44,721 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:44,721 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:44,721 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMLinear
+2017-09-22 15:54:44,722 DEBUG: Start: RandomSearch best settings with 20 iterations for SGD
+2017-09-22 15:54:46,726 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:54:46,727 DEBUG: Start: Training
+2017-09-22 15:54:46,732 DEBUG: Done: Training
+2017-09-22 15:54:46,732 DEBUG: Start: Predicting
+2017-09-22 15:54:46,733 DEBUG: Done: Predicting
+2017-09-22 15:54:46,734 DEBUG: Info: Time for training and predicting: 2.0305621624[s]
+2017-09-22 15:54:46,734 DEBUG: Start: Getting Results
+2017-09-22 15:54:46,765 DEBUG: Done: Getting Results
+2017-09-22 15:54:46,765 INFO: Classification on awaexp database for phog-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.592689295039, with STD : 0.0
+accuracy_score on test : 0.564417177914, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.592689295039
+ - Score on test : 0.564417177914
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.347280334728
+ - Score on test : 0.268041237113
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.347280334728
+ - Score on test : 0.268041237113
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.407310704961
+ - Score on test : 0.435582822086
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.592689295039
+ - Score on test : 0.564417177914
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.28121293038
+ - Score on test : 0.219597524388
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.873684210526
+ - Score on test : 0.838709677419
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.216710182768
+ - Score on test : 0.159509202454
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.592689295039
+ - Score on test : 0.564417177914
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.407310704961
+ - Score on test : 0.435582822086
+
+
+ Classification took 0:00:02
+2017-09-22 15:54:46,766 INFO: Done: Result Analysis
+2017-09-22 15:54:54,621 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:54:54,621 DEBUG: Start: Training
+2017-09-22 15:54:55,155 DEBUG: Done: Training
+2017-09-22 15:54:55,155 DEBUG: Start: Predicting
+2017-09-22 15:54:55,414 DEBUG: Done: Predicting
+2017-09-22 15:54:55,414 DEBUG: Info: Time for training and predicting: 10.7104449272[s]
+2017-09-22 15:54:55,414 DEBUG: Start: Getting Results
+2017-09-22 15:54:55,443 DEBUG: Done: Getting Results
+2017-09-22 15:54:55,443 INFO: Classification on awaexp database for phog-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 0.678851174935, with STD : 0.0
+accuracy_score on test : 0.61963190184, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3750
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.678851174935
+ - Score on test : 0.61963190184
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.715277777778
+ - Score on test : 0.683673469388
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.715277777778
+ - Score on test : 0.683673469388
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.321148825065
+ - Score on test : 0.38036809816
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.678851174935
+ - Score on test : 0.61963190184
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.370020340933
+ - Score on test : 0.26167425641
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.642411642412
+ - Score on test : 0.585152838428
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.806788511749
+ - Score on test : 0.822085889571
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.678851174935
+ - Score on test : 0.61963190184
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.321148825065
+ - Score on test : 0.38036809816
+
+
+ Classification took 0:00:10
+2017-09-22 15:54:55,443 INFO: Done: Result Analysis
+2017-09-22 15:54:55,580 DEBUG: Start: Loading data
+2017-09-22 15:54:55,581 DEBUG: Start: Loading data
+2017-09-22 15:54:55,582 DEBUG: Done: Loading data
+2017-09-22 15:54:55,583 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2017-09-22 15:54:55,583 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:55,583 DEBUG: Done: Loading data
+2017-09-22 15:54:55,583 DEBUG: Info: Classification - Database:awaexp Feature:phog-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2017-09-22 15:54:55,584 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:54:55,597 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:55,598 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:55,598 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:55,598 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMPoly
+2017-09-22 15:54:55,601 DEBUG: Info: Shape X_train:(766, 252), Length of y_train:766
+2017-09-22 15:54:55,601 DEBUG: Info: Shape X_test:(326, 252), Length of y_test:326
+2017-09-22 15:54:55,601 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:54:55,601 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMRBF
+2017-09-22 15:55:08,024 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:55:08,024 DEBUG: Start: Training
+2017-09-22 15:55:08,745 DEBUG: Done: Training
+2017-09-22 15:55:08,745 DEBUG: Start: Predicting
+2017-09-22 15:55:09,099 DEBUG: Done: Predicting
+2017-09-22 15:55:09,099 DEBUG: Info: Time for training and predicting: 13.5177659988[s]
+2017-09-22 15:55:09,099 DEBUG: Start: Getting Results
+2017-09-22 15:55:09,130 DEBUG: Done: Getting Results
+2017-09-22 15:55:09,130 INFO: Classification on awaexp database for phog-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 0.832898172324, with STD : 0.0
+accuracy_score on test : 0.659509202454, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM RBF with C : 1030
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.832898172324
+ - Score on test : 0.659509202454
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.843902439024
+ - Score on test : 0.689075630252
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.843902439024
+ - Score on test : 0.689075630252
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.167101827676
+ - Score on test : 0.340490797546
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.832898172324
+ - Score on test : 0.659509202454
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.672514283873
+ - Score on test : 0.324949230649
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.79176201373
+ - Score on test : 0.634020618557
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.903394255875
+ - Score on test : 0.754601226994
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.832898172324
+ - Score on test : 0.659509202454
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.167101827676
+ - Score on test : 0.340490797546
+
+
+ Classification took 0:00:13
+2017-09-22 15:55:09,130 INFO: Done: Result Analysis
+2017-09-22 15:55:10,542 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:55:10,542 DEBUG: Start: Training
+2017-09-22 15:55:11,389 DEBUG: Done: Training
+2017-09-22 15:55:11,389 DEBUG: Start: Predicting
+2017-09-22 15:55:11,870 DEBUG: Done: Predicting
+2017-09-22 15:55:11,870 DEBUG: Info: Time for training and predicting: 16.2899699211[s]
+2017-09-22 15:55:11,870 DEBUG: Start: Getting Results
+2017-09-22 15:55:11,903 DEBUG: Done: Getting Results
+2017-09-22 15:55:11,903 INFO: Classification on awaexp database for phog-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 0.844647519582, with STD : 0.0
+accuracy_score on test : 0.598159509202, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : phog-hist View shape : (1092, 252)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Poly with C : 3750
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.844647519582
+ - Score on test : 0.598159509202
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.859504132231
+ - Score on test : 0.656167979003
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.859504132231
+ - Score on test : 0.656167979003
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.155352480418
+ - Score on test : 0.401840490798
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.844647519582
+ - Score on test : 0.598159509202
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.705247353516
+ - Score on test : 0.208549836535
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.784482758621
+ - Score on test : 0.573394495413
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.950391644909
+ - Score on test : 0.766871165644
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.844647519582
+ - Score on test : 0.598159509202
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.155352480418
+ - Score on test : 0.401840490798
+
+
+ Classification took 0:00:16
+2017-09-22 15:55:11,904 INFO: Done: Result Analysis
+2017-09-22 15:55:12,063 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:55:12,064 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist, phog-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:55:12,064 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:55:12,064 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist, phog-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:55:12,065 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:55:12,065 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:55:12,065 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:55:12,066 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:55:12,066 INFO: Info: Shape of phog-hist :(1092, 252)
+2017-09-22 15:55:12,066 INFO: Done: Read Database Files
+2017-09-22 15:55:12,066 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:55:12,067 INFO: Info: Shape of phog-hist :(1092, 252)
+2017-09-22 15:55:12,067 INFO: Done: Read Database Files
+2017-09-22 15:55:12,067 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:55:12,131 INFO: Done: Determine validation split
+2017-09-22 15:55:12,131 INFO: Start: Determine 5 folds
+2017-09-22 15:55:12,145 INFO: Done: Determine validation split
+2017-09-22 15:55:12,145 INFO: Start: Determine 5 folds
+2017-09-22 15:58:31,803 INFO: Done: Classification
+2017-09-22 15:58:32,358 INFO: Done: Classification
+2017-09-22 15:58:32,953 INFO: Done: Classification
+2017-09-22 15:58:33,484 INFO: Done: Classification
+2017-09-22 15:58:34,069 INFO: Done: Classification
+2017-09-22 15:58:34,069 INFO: Info: Time for Classification: 202[s]
+2017-09-22 15:58:34,069 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:58:34,744 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.903133159269
+ -On Test : 0.809202453988
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist, phog-hist
+ -5 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.477239577855, 0.236511491673, 0.286248930471
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.903133159269 with STD : 0.0624201116681
+ - Score on test : 0.809202453988 with STD : 0.028140723782
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.912705861478 with STD : 0.0474044942861
+ - Score on test : 0.819195169506 with STD : 0.0145004045657
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.912705861478 with STD : 0.0474044942861
+ - Score on test : 0.819195169506 with STD : 0.0145004045657
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0968668407311 with STD : 0.0624201116681
+ - Score on test : 0.190797546012 with STD : 0.028140723782
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.903133159269 with STD : 0.0624201116681
+ - Score on test : 0.809202453988 with STD : 0.028140723782
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.821343523657 with STD : 0.100808821945
+ - Score on test : 0.631495658738 with STD : 0.0389063078408
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.869561689481 with STD : 0.0934807591073
+ - Score on test : 0.790289416542 with STD : 0.0635398535929
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.969190600522 with STD : 0.0234928916537
+ - Score on test : 0.861349693252 with STD : 0.0677185017254
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.903133159269 with STD : 0.0624201116681
+ - Score on test : 0.809202453988 with STD : 0.028140723782
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0968668407311 with STD : 0.0624201116681
+ - Score on test : 0.190797546012 with STD : 0.028140723782
+
+
+2017-09-22 15:58:34,745 INFO: Done: Result Analysis
+2017-09-22 15:58:41,403 INFO: Done: Classification
+2017-09-22 15:58:41,842 INFO: Done: Classification
+2017-09-22 15:58:42,286 INFO: Done: Classification
+2017-09-22 15:58:42,730 INFO: Done: Classification
+2017-09-22 15:58:43,177 INFO: Done: Classification
+2017-09-22 15:58:43,177 INFO: Info: Time for Classification: 211[s]
+2017-09-22 15:58:43,177 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:58:43,586 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.682506527415
+ -On Test : 0.669938650307
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist, phog-hist
+ -5 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Majority Voting
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.682506527415 with STD : 0.00657424454935
+ - Score on test : 0.669938650307 with STD : 0.00688648598793
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.674439638676 with STD : 0.0295114256546
+ - Score on test : 0.661890004968 with STD : 0.0327476466189
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.674439638676 with STD : 0.0295114256546
+ - Score on test : 0.661890004968 with STD : 0.0327476466189
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.317493472585 with STD : 0.00657424454935
+ - Score on test : 0.330061349693 with STD : 0.00688648598793
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.682506527415 with STD : 0.00657424454935
+ - Score on test : 0.669938650307 with STD : 0.00688648598793
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.369275003784 with STD : 0.0133682817705
+ - Score on test : 0.344948865665 with STD : 0.0143090016406
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.693564492842 with STD : 0.0283381941508
+ - Score on test : 0.681019396548 with STD : 0.0334400547567
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.664229765013 with STD : 0.0770053525556
+ - Score on test : 0.653987730061 with STD : 0.0853268154636
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.682506527415 with STD : 0.00657424454935
+ - Score on test : 0.669938650307 with STD : 0.00688648598793
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.317493472585 with STD : 0.00657424454935
+ - Score on test : 0.330061349693 with STD : 0.00688648598793
+
+
+2017-09-22 15:58:43,587 INFO: Done: Result Analysis
+2017-09-22 15:58:43,657 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:58:43,657 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist, phog-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:58:43,658 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:58:43,659 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist, phog-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:58:43,659 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:58:43,660 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:58:43,660 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:58:43,662 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:58:43,662 INFO: Info: Shape of phog-hist :(1092, 252)
+2017-09-22 15:58:43,662 INFO: Done: Read Database Files
+2017-09-22 15:58:43,662 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:58:43,663 INFO: Info: Shape of phog-hist :(1092, 252)
+2017-09-22 15:58:43,663 INFO: Done: Read Database Files
+2017-09-22 15:58:43,663 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:58:43,755 INFO: Done: Determine validation split
+2017-09-22 15:58:43,755 INFO: Done: Determine validation split
+2017-09-22 15:58:43,755 INFO: Start: Determine 5 folds
+2017-09-22 15:58:43,755 INFO: Start: Determine 5 folds
+2017-09-22 16:03:01,278 INFO: Done: Classification
+2017-09-22 16:03:02,212 INFO: Done: Classification
+2017-09-22 16:03:03,148 INFO: Done: Classification
+2017-09-22 16:03:04,087 INFO: Done: Classification
+2017-09-22 16:03:05,194 INFO: Done: Classification
+2017-09-22 16:03:05,204 INFO: Info: Time for Classification: 261[s]
+2017-09-22 16:03:05,205 INFO: Start: Result Analysis for Fusion
+2017-09-22 16:03:06,079 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.827676240209
+ -On Test : 0.760736196319
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist, phog-hist
+ -5 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SVM for linear
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.827676240209 with STD : 0.0
+ - Score on test : 0.760736196319 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.823056300268 with STD : 1.11022302463e-16
+ - Score on test : 0.75625 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.823056300268 with STD : 1.11022302463e-16
+ - Score on test : 0.75625 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.172323759791 with STD : 0.0
+ - Score on test : 0.239263803681 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.827676240209 with STD : 0.0
+ - Score on test : 0.760736196319 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.656247838383 with STD : 0.0
+ - Score on test : 0.521826039844 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.845730027548 with STD : 0.0
+ - Score on test : 0.770700636943 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.801566579634 with STD : 0.0
+ - Score on test : 0.742331288344 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.827676240209 with STD : 1.11022302463e-16
+ - Score on test : 0.760736196319 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.172323759791 with STD : 0.0
+ - Score on test : 0.239263803681 with STD : 0.0
+
+
+2017-09-22 16:03:06,080 INFO: Done: Result Analysis
+2017-09-22 16:04:28,892 INFO: Done: Classification
+2017-09-22 16:04:29,728 INFO: Done: Classification
+2017-09-22 16:04:30,740 INFO: Done: Classification
+2017-09-22 16:04:31,563 INFO: Done: Classification
+2017-09-22 16:04:32,313 INFO: Done: Classification
+2017-09-22 16:04:32,313 INFO: Info: Time for Classification: 348[s]
+2017-09-22 16:04:32,313 INFO: Start: Result Analysis for Fusion
+2017-09-22 16:04:32,742 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.882506527415
+ -On Test : 0.757668711656
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist, phog-hist
+ -5 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SCM for linear with max_attributes : 14, p : 0.0241145218036 model_type : conjunction has chosen 1 rule(s)
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.882506527415 with STD : 0.0
+ - Score on test : 0.757668711656 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.867256637168 with STD : 1.11022302463e-16
+ - Score on test : 0.710622710623 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.867256637168 with STD : 1.11022302463e-16
+ - Score on test : 0.710622710623 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.117493472585 with STD : 0.0
+ - Score on test : 0.242331288344 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.882506527415 with STD : 0.0
+ - Score on test : 0.757668711656 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.78604273629 with STD : 0.0
+ - Score on test : 0.544949260913 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.996610169492 with STD : 0.0
+ - Score on test : 0.881818181818 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.767624020888 with STD : 0.0
+ - Score on test : 0.59509202454 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.882506527415 with STD : 0.0
+ - Score on test : 0.757668711656 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.117493472585 with STD : 0.0
+ - Score on test : 0.242331288344 with STD : 2.77555756156e-17
+
+
+2017-09-22 16:04:32,742 INFO: Done: Result Analysis
+2017-09-22 16:04:32,827 INFO: ### Main Programm for Multiview Classification
+2017-09-22 16:04:32,828 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist, phog-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 16:04:32,828 INFO: ### Main Programm for Multiview Classification
+2017-09-22 16:04:32,828 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist, phog-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 16:04:32,829 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 16:04:32,830 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 16:04:32,831 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 16:04:32,831 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 16:04:32,831 INFO: Info: Shape of phog-hist :(1092, 252)
+2017-09-22 16:04:32,832 INFO: Done: Read Database Files
+2017-09-22 16:04:32,832 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 16:04:32,832 INFO: Info: Shape of phog-hist :(1092, 252)
+2017-09-22 16:04:32,832 INFO: Done: Read Database Files
+2017-09-22 16:04:32,832 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 16:04:32,889 INFO: Done: Determine validation split
+2017-09-22 16:04:32,889 INFO: Start: Determine 5 folds
+2017-09-22 16:04:32,891 INFO: Done: Determine validation split
+2017-09-22 16:04:32,891 INFO: Start: Determine 5 folds
--
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