Deleted res from git 2

ba8a026a · bbauvin · b2c2cd72 · b2c2cd72 · b2c2cd72 · b2c2cd72
Commit ba8a026a authored Sep 29, 2017 by bbauvin
--- a/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-ClassLabels-Description.csv
+++ b/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-ClassLabels-Description.csv
-0;accordion
-1;anchor
-2;barrel
-3;bass
-4;beaver
-5;binocular
-6;dragonfly
-7;flamingo
--- a/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-ClassLabels.csv
+++ b/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-ClassLabels.csv
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--- a/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-HOG-CellDimension_5-nbOrientaions_8-nbClusters_12-Maxiter_100.csv
+++ b/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-HOG-CellDimension_5-nbOrientaions_8-nbClusters_12-Maxiter_100.csv
--- a/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-HSV-Bins_[8, 3, 3]-Norm_Distr.csv
+++ b/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-HSV-Bins_[8, 3, 3]-Norm_Distr.csv
--- a/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-RGB-Bins_16-MaxCI_256-Norm_Distr.csv
+++ b/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-RGB-Bins_16-MaxCI_256-Norm_Distr.csv
--- a/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-SIFT-Cluster_50-Norm_Distr.csv
+++ b/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-SIFT-Cluster_50-Norm_Distr.csv
--- a/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-SURF-Cluster_50-Norm_Distr.csv
+++ b/Results/Results-FeatExtract-All/2016_02_05-CT-Mini-SURF-Cluster_50-Norm_Distr.csv
--- a/Results/Results-FeatOpt-RGB/2016_01_24-RGB-ClassificationTime.png
+++ b/Results/Results-FeatOpt-RGB/2016_01_24-RGB-ClassificationTime.png
--- a/Results/Results-FeatOpt-RGB/2016_01_24-RGB-FeatExtractTime.png
+++ b/Results/Results-FeatOpt-RGB/2016_01_24-RGB-FeatExtractTime.png
--- a/Results/Results-FeatOpt-RGB/2016_01_24-RGB-TotalTime.png
+++ b/Results/Results-FeatOpt-RGB/2016_01_24-RGB-TotalTime.png
--- a/Results/Results-FeatOpt-RGB/2016_01_24-Results-RGB.csv
+++ b/Results/Results-FeatOpt-RGB/2016_01_24-Results-RGB.csv
-;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score
-0;CT-RGB-Bins_8-MaxCI_256-Norm_Distr;16,071;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";29,709;0,276704
-1;CT-RGB-Bins_16-MaxCI_256-Norm_Distr;17,192;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";42,755;0,296391
-2;CT-RGB-Bins_24-MaxCI_256-Norm_Distr;19,387;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";64,978;0,312796
-3;CT-RGB-Bins_32-MaxCI_256-Norm_Distr;24,506;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";74,039;0,321546
-4;CT-RGB-Bins_40-MaxCI_256-Norm_Distr;26,119;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";81,705;0,319723
-5;CT-RGB-Bins_48-MaxCI_256-Norm_Distr;26,326;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";95,86;0,287277
-6;CT-RGB-Bins_56-MaxCI_256-Norm_Distr;22,426;Classif_RF-CV_5-Trees_100;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";89,742;0,302588
-7;CT-RGB-Bins_64-MaxCI_256-Norm_Distr;142,262;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 100, 150]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";103,376;0,310244
--- a/Results/Results-FeatOpt-SURF/2016_01_28-Results-SURF.csv
+++ b/Results/Results-FeatOpt-SURF/2016_01_28-Results-SURF.csv
-;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score
-0;CT-SURF-Cluster_50.0-Norm_Distr;1371,7;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 101, 150, 200]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";79,638;0,327014
-1;CT-SURF-Cluster_100.0-Norm_Distr;8495,44;Classif_RF-CV_5-Trees_200;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 101, 150, 200]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";111,646;0,329202
-2;CT-SURF-Cluster_150.0-Norm_Distr;41403,7;Classif_RF-CV_5-Trees_200;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 101, 150, 200]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";142,393;0,33467
-3;CT-SURF-Cluster_200.0-Norm_Distr;59108,8;Classif_RF-CV_5-Trees_150;"GridSearchCV(cv=5, error_score='raise',
-       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
-            max_depth=None, max_features='auto', max_leaf_nodes=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
-            oob_score=False, random_state=None, verbose=0,
-            warm_start=False))]),
-       fit_params={}, iid=True, loss_func=None, n_jobs=1,
-       param_grid={'classifier__n_estimators': [50, 101, 150, 200]},
-       pre_dispatch='2*n_jobs', refit=True, score_func=None,
-       scoring='accuracy', verbose=0)";137,585;0,319358
--- a/Results/Results-FeatOpt-SURF/Console-Output.txt
+++ b/Results/Results-FeatOpt-SURF/Console-Output.txt
-D:\Programme\Anaconda\Scripts>ipython.exe D:\09-Py\ExecFeatParaOpt.py
-### Start of Main Programm for Feature Parameter Optimisation ###
-### Start:       Exportation of images from DB ###
-### Done:        Exportation of Images from DB ###
-### Optimisation - Feature:SURF Parameter:Cluster from:50 to:200 in #calc:4 with
-CV:True ###
-### Start:       Feautre Optimisation ###
-### Start:       FeatureExtraction Nr:1 from:4 with:Cluster=50.0 ###
-SURF: Keypoints Calculation
-SURF: 25% of Images processed (Keypoints)
-SURF: 50% of Images processed (Keypoints)
-SURF: 75% of Images processed (Keypoints)
-SURF: Length of Descriptors: 5546033
-SURF: Start filling descriptors
-SURF: Shape of Descriptors: (5546033L, 64L)
-Begin of SURFSIFTHisto
-SURF: Calculation of Dictonary with 50 Clusters
-SURF: Assign words from Dictonary to each Image
-SURF25% of Images processed (Assignments)
-SURF50% of Images processed (Assignments)
-SURF75% of Images processed (Assignments)
-### Done:        FeatureExtraction Nr:1 from:4 ###
-### Start:       Classification Nr:1 from:4 ###
-D:\Programme\Anaconda\lib\site-packages\sklearn\cross_validation.py:417: Warning
-: The least populated class in y has only 4 members, which is too few. The minim
-um number of labels for any class cannot be less than n_folds=5.
-  % (min_labels, self.n_folds)), Warning)
-### Done:        Classification Nr:1 from:4 ###
-### Start:       FeatureExtraction Nr:2 from:4 with:Cluster=100.0 ###
-Begin of SURFSIFTHisto
-SURF: Calculation of Dictonary with 100 Clusters
-SURF: Assign words from Dictonary to each Image
-SURF25% of Images processed (Assignments)
-SURF50% of Images processed (Assignments)
-SURF75% of Images processed (Assignments)
-### Done:        FeatureExtraction Nr:2 from:4 ###
-### Start:       Classification Nr:2 from:4 ###
-### Done:        Classification Nr:2 from:4 ###
-### Start:       FeatureExtraction Nr:3 from:4 with:Cluster=150.0 ###
-Begin of SURFSIFTHisto
-SURF: Calculation of Dictonary with 150 Clusters
-SURF: Assign words from Dictonary to each Image
-SURF25% of Images processed (Assignments)
-SURF50% of Images processed (Assignments)
-SURF75% of Images processed (Assignments)
-### Done:        FeatureExtraction Nr:3 from:4 ###
-### Start:       Classification Nr:3 from:4 ###
-### Done:        Classification Nr:3 from:4 ###
-### Start:       FeatureExtraction Nr:4 from:4 with:Cluster=200.0 ###
-Begin of SURFSIFTHisto
-SURF: Calculation of Dictonary with 200 Clusters
-SURF: Assign words from Dictonary to each Image
-SURF25% of Images processed (Assignments)
-SURF50% of Images processed (Assignments)
-SURF75% of Images processed (Assignments)
-### Done:        FeatureExtraction Nr:4 from:4 ###
-### Start:       Classification Nr:4 from:4 ###
-### Done:        Classification Nr:4 from:4 ###
-### Done:        Feautre Optimisation ###
-### Start:       Exporting to CSV ###
-### Done:        Exporting to CSV ###
--- a/Results/Results-FeatOpt-SURF/ScoreClassificationTime.png
+++ b/Results/Results-FeatOpt-SURF/ScoreClassificationTime.png
--- a/Results/Results-FeatOpt-SURF/ScoreFeatExtractionTime.png
+++ b/Results/Results-FeatOpt-SURF/ScoreFeatExtractionTime.png
--- a/Results/Results-FeatOpt-SURF/ScoreTotalTime.png
+++ b/Results/Results-FeatOpt-SURF/ScoreTotalTime.png