diff --git a/Code/FeatExtraction/ExecFeatParaOpt.py b/Code/FeatExtraction/ExecFeatParaOpt.py
index dbc67f2d35a725a1b321aa56a405c2e64f6dd23b..4c9e8a34555eaf6066fdd86baaa8d0aa89d9a343 100644
--- a/Code/FeatExtraction/ExecFeatParaOpt.py
+++ b/Code/FeatExtraction/ExecFeatParaOpt.py
@@ -147,12 +147,13 @@ cl_desc = df_feat_res.c_cl_desc.values
# Description of Feature
feat_desc = df_feat_res.a_feat_desc.values
+store = True
fileName = dir + datetime.datetime.now().strftime("%Y_%m_%d") + "-" + "Feature_" + args.feature + "-Parameter_" + args.param
# Show Results for Calculation
-ExportResults.showScoreTime(fileName + "-TotalTime.png", score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
-ExportResults.showScoreTime(fileName + "-FeatExtTime.png", score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Feature Extraction Time\n [s]')
-ExportResults.showScoreTime(fileName + "-ClassTime.png", score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Classification Time\n [s]')
+ExportResults.showScoreTime(fileName + "-TotalTime.png", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
+ExportResults.showScoreTime(fileName + "-FeatExtTime.png", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Feature Extraction Time\n [s]')
+ExportResults.showScoreTime(fileName + "-ClassTime.png", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Classification Time\n [s]')
diff --git a/Code/FeatExtraction/ExecPlot.py b/Code/FeatExtraction/ExecPlot.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fae24536c11096e5d4433e5304d0626f0d40c71
--- /dev/null
+++ b/Code/FeatExtraction/ExecPlot.py
@@ -0,0 +1,57 @@
+import pandas as pd
+import numpy as np
+import datetime
+import argparse # for acommand line arguments
+import os # to geth path of the running script
+import ExportResults # Functions to render results
+
+parser = argparse.ArgumentParser(
+description='This methods permits to execute a multiclass classification with one single view. At this point the used classifier is a RandomForest. The GridSearch permits to vary the number of trees and CrossValidation with k-folds.',
+formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+args = parser.parse_args()
+args.valueEnd = 5
+args.valueStart =75
+args.nCalcs = 8
+args.feature = "HOG"
+args.param = "HOG_Cluster"
+df_feat_res = pd.DataFrame.from_csv(path="D:\\BitBucket\\multiview-machine-learning-omis\\Code\\FeatExtraction\\Results-FeatParaOpt\\2016_03_19-FeatParaOpt-HOG.csv", sep=';')
+
+# Get data from result to show results in plot
+# Total time for feature extraction and classification
+tot_time = df_feat_res.b_feat_extr_time.values + df_feat_res.e_cl_time.values
+tot_time = np.asarray(tot_time)
+# Time for feature extraction
+feat_time = df_feat_res.b_feat_extr_time.values
+feat_time = np.asarray(feat_time)
+# Time for classification
+cl_time = df_feat_res.e_cl_time.values
+cl_time = np.asarray(cl_time)
+
+# Mean Score of all classes
+score = df_feat_res.f_cl_score.values
+score = np.asarray(score)
+
+
+# Range on X-Axis
+if(args.nCalcs>1):
+ step = float(args.valueEnd-args.valueStart)/float(args.nCalcs-1)
+ rangeX = np.around(np.array(range(0,args.nCalcs))*step) + args.valueStart
+else:
+ rangeX = [args.valueStart]
+rangeX = np.asarray(rangeX)
+
+# Description of Classification
+cl_desc = df_feat_res.c_cl_desc.values
+
+# Description of Feature
+feat_desc = df_feat_res.a_feat_desc.values
+
+dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatParaOpt/"
+
+fileName = dir + datetime.datetime.now().strftime("%Y_%m_%d") + "-" + "Feature_" + args.feature + "-Parameter_" + args.param
+store = False
+
+# Show Results for Calculation
+ExportResults.showScoreTime(fileName + "-TotalTime.png", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
+ExportResults.showScoreTime(fileName + "-FeatExtTime.png", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Feature Extraction Time\n [s]')
+ExportResults.showScoreTime(fileName + "-ClassTime.png", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Classification Time\n [s]')
\ No newline at end of file
diff --git a/Code/FeatExtraction/ExportResults.py b/Code/FeatExtraction/ExportResults.py
index 168ecd6cbb8b961c6ac5e4a73962143db6e00422..e6d1ef2a3849fe86e02e00bfb56251247524f31f 100644
--- a/Code/FeatExtraction/ExportResults.py
+++ b/Code/FeatExtraction/ExportResults.py
@@ -28,11 +28,11 @@ def exportPandasToCSV(pandasSorDF, dir, filename):
for i in range(1,20):
testFileName = filename + "-" + str(i) + ".csv"
if os.path.isfile(dir + testFileName)!=True:
- pandasSorDF.to_csv(dir + testFileName, sep=',')
+ pandasSorDF.to_csv(dir + testFileName, sep=';')
break
else:
- pandasSorDF.to_csv(file + ".csv", sep=',')
+ pandasSorDF.to_csv(file + ".csv", sep=';')
def exportNumpyToCSV(numpyArray, dir, filename, format):
@@ -42,17 +42,17 @@ def exportNumpyToCSV(numpyArray, dir, filename, format):
for i in range(1,20):
testFileName = filename + "-" + str(i) + ".csv"
if os.path.isfile(dir + testFileName )!=True:
- np.savetxt(dir + testFileName, numpyArray, delimiter=",", fmt=format)
+ np.savetxt(dir + testFileName, numpyArray, delimiter=";", fmt=format)
break
else:
- np.savetxt(file + ".csv", numpyArray, delimiter=",", fmt=format)
+ np.savetxt(file + ".csv", numpyArray, delimiter=";", fmt=format)
#### Rendering of results
### Rendering of Score and Time
-def showScoreTime(filename, resScore, resTime, rangeX, parameter, feat_desc, cl_desc, fig_desc, y_desc1, y_desc2):
+def showScoreTime(filename, store, resScore, resTime, rangeX, parameter, feat_desc, cl_desc, fig_desc, y_desc1, y_desc2):
# Determine interpolated functions
f_score_interp = interp1d(rangeX, resScore, kind='quadratic')
f_time_interp = interp1d(rangeX, resTime, kind='quadratic')
@@ -110,14 +110,12 @@ def showScoreTime(filename, resScore, resTime, rangeX, parameter, feat_desc, cl_
ax2.legend(['Time Data', 'Time Interpolated'], loc='lower right')
plt.title(fig_desc, fontsize=18)
-
- plt.savefig(filename)
-
- # instead of saving - decomment plt.show()
- # plt.show()
-
-
+ if(store):
+ plt.savefig(filename)
+ else:
+ plt.show()
+
### Result comparision per class
def calcScorePerClass(np_labels, np_output):
diff --git a/Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG-1.csv b/Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..81a5d8e8354291d3910e3a362d1f15a719076cd0
--- /dev/null
+++ b/Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG-1.csv
@@ -0,0 +1,146 @@
+;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score
+0;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_10-Maxiter_100;919.0956721305847;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";184.36450791358948;0.36555023923444974
+1;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_20-Maxiter_100;902.1321420669556;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";234.061616897583;0.3949419002050581
+2;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_30-Maxiter_100;1091.6442930698395;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";283.3998420238495;0.4157211209842789
+3;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_40-Maxiter_100;1120.3388640880585;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";329.4008128643036;0.41066302118933695
+4;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_50-Maxiter_100;1151.4867160320282;Classif_RF-CV_8-Trees_150;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";407.7028250694275;0.40437457279562544
diff --git a/Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG.csv b/Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG.csv
new file mode 100644
index 0000000000000000000000000000000000000000..46387b61ca08d42795334675cdd7eb88a2896410
--- /dev/null
+++ b/Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG.csv
@@ -0,0 +1,233 @@
+;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score
+0;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_5-Maxiter_100;909.2955379486084;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";125.15065908432007;0.2530416951469583
+1;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_15-Maxiter_100;935.2981481552124;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";183.69755601882935;0.382365003417635
+2;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_25-Maxiter_100;1027.5009920597076;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";283.1033492088318;0.3979494190020506
+3;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_35-Maxiter_100;962.9278299808502;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
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diff --git a/Code/FeatExtraction/Results-FeatParaOpt/2016_03_20-FeatParaOpt-HSV-H_Bins.csv b/Code/FeatExtraction/Results-FeatParaOpt/2016_03_20-FeatParaOpt-HSV-H_Bins.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8d12803576738e3e4292fdb9a1ecc9d801f9d750
--- /dev/null
+++ b/Code/FeatExtraction/Results-FeatParaOpt/2016_03_20-FeatParaOpt-HSV-H_Bins.csv
@@ -0,0 +1,726 @@
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+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=100,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";74.63008189201355;0.3043062200956938
+23;Caltech-HSV-Bins_[24, 4, 4]-Norm_Distr;29.990907907485962;Classif_RF-CV_8-Trees_150;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=100,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";74.13478493690491;0.3079972658920027
+24;Caltech-HSV-Bins_[25, 4, 4]-Norm_Distr;33.7245659828186;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8,
+ estimator=Pipeline(classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=No...jobs=1,
+ classifier__oob_score=False, classifier__random_state=None,
+ classifier__verbose=0),
+ estimator__classifier=RandomForestClassifier(bootstrap=True, compute_importances=None,
+ criterion=gini, max_depth=None, max_features=auto,
+ min_density=None, min_samples_leaf=1, min_samples_split=2,
+ n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
+ verbose=0),
+ estimator__classifier__bootstrap=True,
+ estimator__classifier__compute_importances=None,
+ estimator__classifier__criterion=gini,
+ estimator__classifier__max_depth=None,
+ estimator__classifier__max_features=auto,
+ estimator__classifier__min_density=None,
+ estimator__classifier__min_samples_leaf=1,
+ estimator__classifier__min_samples_split=2,
+ estimator__classifier__n_estimators=10,
+ estimator__classifier__n_jobs=1,
+ estimator__classifier__oob_score=False,
+ estimator__classifier__random_state=None,
+ estimator__classifier__verbose=0, fit_params={}, iid=True,
+ loss_func=None, n_jobs=100,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch=2*n_jobs, refit=True, score_func=None,
+ scoring=accuracy, verbose=0)";89.30819916725159;0.30006835269993165
diff --git a/Code/FeatExtraction/__init__.py b/Code/FeatExtraction/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cf13eb01a0a00ad42d8f1090447d27520222421
--- /dev/null
+++ b/Code/FeatExtraction/__init__.py
@@ -0,0 +1 @@
+# Init
\ No newline at end of file