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, + 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)";321.2544548511505;0.40833902939166095 +4;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_45-Maxiter_100;1186.1991739273071;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, + 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score_func=None, + scoring=accuracy, verbose=0)";399.62198305130005;0.40847573479152427 +6;Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_65-Maxiter_100;1333.6005101203918;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, + 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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)";445.3156099319458;0.4079289131920711 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 @@ +;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score +0;Caltech-HSV-Bins_[2, 4, 4]-Norm_Distr;26.35770583152771;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)";25.30480408668518;0.2690362269309638 +1;Caltech-HSV-Bins_[3, 4, 4]-Norm_Distr;22.307148933410645;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, 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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)";30.78425097465515;0.28940533151059467 +4;Caltech-HSV-Bins_[6, 4, 4]-Norm_Distr;28.441756010055542;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)";32.98688292503357;0.29801777170198224 +5;Caltech-HSV-Bins_[7, 4, 4]-Norm_Distr;30.00211000442505;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)";38.095832109451294;0.3053998632946001 +6;Caltech-HSV-Bins_[8, 4, 4]-Norm_Distr;26.41002893447876;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), + 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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)";47.11957883834839;0.3064935064935065 +8;Caltech-HSV-Bins_[10, 4, 4]-Norm_Distr;30.382289171218872;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)";45.70924186706543;0.3064935064935065 +9;Caltech-HSV-Bins_[11, 4, 4]-Norm_Distr;27.665060997009277;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)";48.79233503341675;0.3089542036910458 +10;Caltech-HSV-Bins_[12, 4, 4]-Norm_Distr;33.639941930770874;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)";57.601593017578125;0.3152426520847573 +11;Caltech-HSV-Bins_[13, 4, 4]-Norm_Distr;29.85008192062378;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)";51.81808686256409;0.31100478468899523 +12;Caltech-HSV-Bins_[14, 4, 4]-Norm_Distr;27.584290981292725;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)";52.568333864212036;0.3069036226930964 +13;Caltech-HSV-Bins_[14, 4, 4]-Norm_Distr;32.55683398246765;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)";50.82839608192444;0.3067669172932331 +14;Caltech-HSV-Bins_[15, 4, 4]-Norm_Distr;29.72573184967041;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)";57.25728893280029;0.3019822282980178 +15;Caltech-HSV-Bins_[16, 4, 4]-Norm_Distr;28.662791967391968;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)";57.10329604148865;0.3097744360902256 +16;Caltech-HSV-Bins_[17, 4, 4]-Norm_Distr;28.73147201538086;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)";61.18061399459839;0.30963773069036227 +17;Caltech-HSV-Bins_[18, 4, 4]-Norm_Distr;29.023580074310303;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)";68.52751803398132;0.3063568010936432 +18;Caltech-HSV-Bins_[19, 4, 4]-Norm_Distr;29.128270864486694;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)";77.0263659954071;0.3086807928913192 +19;Caltech-HSV-Bins_[20, 4, 4]-Norm_Distr;29.637210845947266;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)";66.19223308563232;0.30731373889268626 +20;Caltech-HSV-Bins_[21, 4, 4]-Norm_Distr;29.10114598274231;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)";72.34151220321655;0.3089542036910458 +21;Caltech-HSV-Bins_[22, 4, 4]-Norm_Distr;28.513449907302856;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)";73.81333589553833;0.3090909090909091 +22;Caltech-HSV-Bins_[23, 4, 4]-Norm_Distr;28.733075857162476;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)";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