From 5e599dcf5378675557fec2151be9baded49fd558 Mon Sep 17 00:00:00 2001 From: Antoine Barbez Date: Fri, 31 May 2019 18:52:50 -0400 Subject: [PATCH] redid tuning for incode, hist and asci --- .DS_Store | Bin 6148 -> 6148 bytes experiments/study_results/context.py | 3 +- experiments/study_results/perfs_god_class.py | 11 +- experiments/training/train_asci.py | 14 +- experiments/tuning/context.py | 9 +- .../android-frameworks-opt-telephony.csv | 201 ++++++++++++++++++ .../feature_envy/android-platform-support.csv | 201 ++++++++++++++++++ .../results/asci/feature_envy/apache-ant.csv | 201 ++++++++++++++++++ .../asci/feature_envy/apache-tomcat.csv | 201 ++++++++++++++++++ .../results/asci/feature_envy/argouml.csv | 201 ++++++++++++++++++ .../results/asci/feature_envy/jedit.csv | 201 ++++++++++++++++++ .../results/asci/feature_envy/lucene.csv | 201 ++++++++++++++++++ .../asci/feature_envy/xerces-2_7_0.csv | 201 ++++++++++++++++++ .../android-frameworks-opt-telephony.csv | 201 ++++++++++++++++++ .../god_class/android-platform-support.csv | 201 ++++++++++++++++++ .../results/asci/god_class/apache-ant.csv | 201 ++++++++++++++++++ .../results/asci/god_class/apache-tomcat.csv | 201 ++++++++++++++++++ .../tuning/results/asci/god_class/argouml.csv | 201 ++++++++++++++++++ .../tuning/results/asci/god_class/jedit.csv | 201 ++++++++++++++++++ .../tuning/results/asci/god_class/lucene.csv | 201 ++++++++++++++++++ .../results/asci/god_class/xerces-2_7_0.csv | 201 ++++++++++++++++++ .../android-frameworks-opt-telephony.csv | 21 ++ .../feature_envy/android-platform-support.csv | 21 ++ .../results/hist/feature_envy/apache-ant.csv | 21 ++ .../hist/feature_envy/apache-tomcat.csv | 21 ++ .../results/hist/feature_envy/argouml.csv | 21 ++ .../results/hist/feature_envy/jedit.csv | 21 ++ .../results/hist/feature_envy/lucene.csv | 21 ++ .../hist/feature_envy/xerces-2_7_0.csv | 21 ++ .../android-frameworks-opt-telephony.csv | 41 ++++ .../god_class/android-platform-support.csv | 41 ++++ .../results/hist/god_class/apache-ant.csv | 41 ++++ .../results/hist/god_class/apache-tomcat.csv | 41 ++++ .../tuning/results/hist/god_class/argouml.csv | 41 ++++ .../tuning/results/hist/god_class/jedit.csv | 41 ++++ .../tuning/results/hist/god_class/lucene.csv | 41 ++++ .../results/hist/god_class/xerces-2_7_0.csv | 41 ++++ .../android-frameworks-opt-telephony.csv | 126 +++++++++++ .../incode/android-platform-support.csv | 126 +++++++++++ .../tuning/results/incode/apache-ant.csv | 126 +++++++++++ .../tuning/results/incode/apache-tomcat.csv | 126 +++++++++++ experiments/tuning/results/incode/argouml.csv | 126 +++++++++++ experiments/tuning/results/incode/jedit.csv | 126 +++++++++++ experiments/tuning/results/incode/lucene.csv | 126 +++++++++++ .../tuning/results/incode/xerces-2_7_0.csv | 126 +++++++++++ .../tuning/results/smad/god_class/lucene.csv | 27 +++ experiments/tuning/tune_asci.py | 69 ++++-- experiments/tuning/tune_hist.py | 64 ++++++ experiments/tuning/tune_incode.py | 64 ++++++ experiments/tuning/tune_smad.py | 89 ++++---- neural_networks/asci/context.py | 12 +- neural_networks/asci/predict.py | 14 +- neural_networks/asci/trained_models/.DS_Store | Bin 6148 -> 6148 bytes .../trained_models/feature_envy/.DS_Store | Bin 0 -> 6148 bytes .../asci/trained_models/god_class/.DS_Store | Bin 0 -> 6148 bytes neural_networks/hist/context.py | 3 +- neural_networks/hist/detect_feature_envy.py | 11 +- neural_networks/hist/detect_god_class.py | 14 +- neural_networks/incode/context.py | 3 +- neural_networks/incode/detect.py | 9 +- neural_networks/smad/predict.py | 14 +- .../trained_models/god_class/jedit/checkpoint | 11 - .../jedit/model_0.data-00000-of-00001 | Bin 2644 -> 0 bytes .../god_class/jedit/model_0.index | Bin 224 -> 0 bytes .../god_class/jedit/model_0.meta | Bin 51277 -> 0 bytes .../jedit/model_1.data-00000-of-00001 | Bin 2644 -> 0 bytes .../god_class/jedit/model_1.index | Bin 224 -> 0 bytes .../god_class/jedit/model_1.meta | Bin 51381 -> 0 bytes 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.../god_class/jedit/model_7.meta | Bin 52005 -> 0 bytes .../jedit/model_8.data-00000-of-00001 | Bin 2644 -> 0 bytes .../god_class/jedit/model_8.index | Bin 224 -> 0 bytes .../god_class/jedit/model_8.meta | Bin 52109 -> 0 bytes .../jedit/model_9.data-00000-of-00001 | Bin 2644 -> 0 bytes .../god_class/jedit/model_9.index | Bin 224 -> 0 bytes .../god_class/jedit/model_9.meta | Bin 52213 -> 0 bytes utils/nnUtils.py | 64 ++++-- 93 files changed, 5083 insertions(+), 142 deletions(-) create mode 100644 experiments/tuning/results/asci/feature_envy/android-frameworks-opt-telephony.csv create mode 100644 experiments/tuning/results/asci/feature_envy/android-platform-support.csv create mode 100644 experiments/tuning/results/asci/feature_envy/apache-ant.csv create mode 100644 experiments/tuning/results/asci/feature_envy/apache-tomcat.csv create mode 100644 experiments/tuning/results/asci/feature_envy/argouml.csv create mode 100644 experiments/tuning/results/asci/feature_envy/jedit.csv create mode 100644 experiments/tuning/results/asci/feature_envy/lucene.csv create mode 100644 experiments/tuning/results/asci/feature_envy/xerces-2_7_0.csv create mode 100644 experiments/tuning/results/asci/god_class/android-frameworks-opt-telephony.csv create mode 100644 experiments/tuning/results/asci/god_class/android-platform-support.csv create mode 100644 experiments/tuning/results/asci/god_class/apache-ant.csv create mode 100644 experiments/tuning/results/asci/god_class/apache-tomcat.csv create mode 100644 experiments/tuning/results/asci/god_class/argouml.csv create mode 100644 experiments/tuning/results/asci/god_class/jedit.csv create mode 100644 experiments/tuning/results/asci/god_class/lucene.csv create mode 100644 experiments/tuning/results/asci/god_class/xerces-2_7_0.csv create mode 100644 experiments/tuning/results/hist/feature_envy/android-frameworks-opt-telephony.csv create mode 100644 experiments/tuning/results/hist/feature_envy/android-platform-support.csv create mode 100644 experiments/tuning/results/hist/feature_envy/apache-ant.csv create mode 100644 experiments/tuning/results/hist/feature_envy/apache-tomcat.csv create mode 100644 experiments/tuning/results/hist/feature_envy/argouml.csv create mode 100644 experiments/tuning/results/hist/feature_envy/jedit.csv create mode 100644 experiments/tuning/results/hist/feature_envy/lucene.csv create mode 100644 experiments/tuning/results/hist/feature_envy/xerces-2_7_0.csv create mode 100644 experiments/tuning/results/hist/god_class/android-frameworks-opt-telephony.csv create mode 100644 experiments/tuning/results/hist/god_class/android-platform-support.csv create mode 100644 experiments/tuning/results/hist/god_class/apache-ant.csv create mode 100644 experiments/tuning/results/hist/god_class/apache-tomcat.csv create mode 100644 experiments/tuning/results/hist/god_class/argouml.csv create mode 100644 experiments/tuning/results/hist/god_class/jedit.csv create mode 100644 experiments/tuning/results/hist/god_class/lucene.csv create mode 100644 experiments/tuning/results/hist/god_class/xerces-2_7_0.csv create mode 100644 experiments/tuning/results/incode/android-frameworks-opt-telephony.csv create mode 100644 experiments/tuning/results/incode/android-platform-support.csv create mode 100644 experiments/tuning/results/incode/apache-ant.csv create mode 100644 experiments/tuning/results/incode/apache-tomcat.csv create mode 100644 experiments/tuning/results/incode/argouml.csv create mode 100644 experiments/tuning/results/incode/jedit.csv create mode 100644 experiments/tuning/results/incode/lucene.csv create mode 100644 experiments/tuning/results/incode/xerces-2_7_0.csv create mode 100644 experiments/tuning/results/smad/god_class/lucene.csv create mode 100644 experiments/tuning/tune_hist.py create mode 100644 experiments/tuning/tune_incode.py create mode 100644 neural_networks/asci/trained_models/feature_envy/.DS_Store create mode 100644 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GIT binary patch delta 20 bcmZoMXffC@mywZi@;pX+M#jze7}dl8M6Ct- delta 20 bcmZoMXffC@mywZS@;pX+MuyGz7}dl8M4JWp diff --git a/experiments/study_results/context.py b/experiments/study_results/context.py index bb9167b..3388964 100644 --- a/experiments/study_results/context.py +++ b/experiments/study_results/context.py @@ -14,4 +14,5 @@ import neural_networks.hist.detect_feature_envy as hist_fe import neural_networks.jdeodorant.detect_feature_envy as jdeodorant_fe -import neural_networks.vote.detect as vote \ No newline at end of file +import neural_networks.vote.detect as vote +import neural_networks.asci.predict as asci \ No newline at end of file diff --git a/experiments/study_results/perfs_god_class.py b/experiments/study_results/perfs_god_class.py index af4c98b..922fb04 100644 --- a/experiments/study_results/perfs_god_class.py +++ b/experiments/study_results/perfs_god_class.py @@ -1,4 +1,4 @@ -from context import nnUtils, decor, hist_gc, jdeodorant_gc, vote +from context import nnUtils, decor, hist_gc, jdeodorant_gc, vote, asci import numpy as np @@ -17,6 +17,7 @@ overall_prediction_hist = np.empty(shape=[0, 1]) overall_prediction_jd = np.empty(shape=[0, 1]) overall_prediction_vote = np.empty(shape=[0, 1]) +overall_prediction_asci = np.empty(shape=[0, 1]) overall_labels = np.empty(shape=[0, 1]) for system in systems: @@ -40,6 +41,10 @@ prediction_vote = nnUtils.predictFromDetect('god_class', system, vote.detect('god_class', system)) overall_prediction_vote = np.concatenate((overall_prediction_vote, prediction_vote), axis=0) + # Compute performances for ASCI + prediction_asci = asci.predict('god_class', system) + overall_prediction_asci = np.concatenate((overall_prediction_asci, prediction_asci), axis=0) + # Print performances for the considered system print(system) print(' |precision |recall |f_measure') @@ -52,6 +57,8 @@ print('-------------------------------------------') print('Vote |' + "{0:.3f}".format(nnUtils.precision(prediction_vote, labels)) + ' |' + "{0:.3f}".format(nnUtils.recall(prediction_vote, labels)) + ' |' + "{0:.3f}".format(nnUtils.f_measure(prediction_vote, labels))) print('-------------------------------------------') + print('ASCI |' + "{0:.3f}".format(nnUtils.precision(prediction_asci, labels)) + ' |' + "{0:.3f}".format(nnUtils.recall(prediction_asci, labels)) + ' |' + "{0:.3f}".format(nnUtils.f_measure(prediction_asci, labels))) + print('-------------------------------------------') print('\n') @@ -67,3 +74,5 @@ print('-------------------------------------------') print('Vote |' + "{0:.3f}".format(nnUtils.precision(overall_prediction_vote, overall_labels)) + ' |' + "{0:.3f}".format(nnUtils.recall(overall_prediction_vote, overall_labels)) + ' |' + "{0:.3f}".format(nnUtils.f_measure(overall_prediction_vote, overall_labels))) print('-------------------------------------------') +print('ASCI |' + "{0:.3f}".format(nnUtils.precision(overall_prediction_asci, overall_labels)) + ' |' + "{0:.3f}".format(nnUtils.recall(overall_prediction_asci, overall_labels)) + ' |' + "{0:.3f}".format(nnUtils.f_measure(overall_prediction_asci, overall_labels))) +print('-------------------------------------------') diff --git a/experiments/training/train_asci.py b/experiments/training/train_asci.py index f74c05c..e67137d 100644 --- a/experiments/training/train_asci.py +++ b/experiments/training/train_asci.py @@ -22,10 +22,10 @@ def parse_args(): parser.add_argument("antipattern", help="Either 'god_class' or 'feature_envy'.") parser.add_argument("test_system", help="The name of the system to be used for testing.\n Hence, the training will be performed using all the systems except this one.") parser.add_argument("-n_tree", type=int, default=10, help="The number of distinct trees to be trained and saved.") - parser.add_argument("-min_samples_split", type=int, default=5) - parser.add_argument("-max_features", default='log2') + parser.add_argument("-min_samples_split", type=float, default=0.01) + parser.add_argument("-max_features", default=None) parser.add_argument("-max_depth", type=int, default=None) - parser.add_argument("-min_samples_leaf", type=int, default=2) + parser.add_argument("-min_samples_leaf", type=int, default=1) return parser.parse_args() # Build the dataset for asci, i.e., the labels are the indexes of the best tool for each input instance. @@ -35,7 +35,7 @@ def parse_args(): # idx = 2: JDeodorant def build_asci_dataset(antipattern, systems): # Get real instances and labels - instances, labels = nnUtils.build_dataset(antipattern, systems) + instances, labels = nnUtils.build_dataset(antipattern, systems, True) # Compute the performances of each tool in order to sort them accordingly nb_tools = 3 @@ -46,7 +46,7 @@ def build_asci_dataset(antipattern, systems): toolsOverallPredictions[i] = np.concatenate((toolsOverallPredictions[i], toolsPredictions[i]), axis=0) toolsPerformances = [nnUtils.f_measure(pred, labels) for pred in toolsOverallPredictions] - + # Indexes of the tools, sorted according to their performances on the training set toolsSortedIndexes = np.argsort(np.array(toolsPerformances)) @@ -68,9 +68,9 @@ def build_asci_dataset(antipattern, systems): # Remove the test system from the training set and build dataset training_systems.remove(args.test_system) x_train, y_train = build_asci_dataset(args.antipattern, training_systems) - + # Test dataset, note that here y_test contains the real labels while y_train contains tools' indexes - x_test, y_test = nnUtils.build_dataset(args.antipattern, [args.test_system]) + x_test, y_test = nnUtils.build_dataset(args.antipattern, [args.test_system], True) toolsPredictions = asci.getToolsPredictions(args.antipattern, args.test_system) # Train and compute ensemble prediction on test set diff --git a/experiments/tuning/context.py b/experiments/tuning/context.py index adb0189..f90e8ff 100644 --- a/experiments/tuning/context.py +++ b/experiments/tuning/context.py @@ -6,6 +6,9 @@ import utils.nnUtils as nnUtils -import neural_networks.asci.predict as asci -import neural_networks.smad.model as md -import neural_networks.vote.detect as vote \ No newline at end of file +import neural_networks.asci.predict as asci +import neural_networks.smad.model as md +import neural_networks.vote.detect as vote +import neural_networks.incode.detect as incode +import neural_networks.hist.detect_god_class as hist_gc +import neural_networks.hist.detect_feature_envy as hist_fe \ No newline at end of file diff --git a/experiments/tuning/results/asci/feature_envy/android-frameworks-opt-telephony.csv b/experiments/tuning/results/asci/feature_envy/android-frameworks-opt-telephony.csv new file mode 100644 index 0000000..c4405ae --- /dev/null +++ b/experiments/tuning/results/asci/feature_envy/android-frameworks-opt-telephony.csv @@ -0,0 +1,201 @@ +Max features;Max depth;Min samples leaf;Min samples split;Accuracy +None;20;3;0.00392337327543;0.9942577426339075 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a/experiments/tuning/results/incode/lucene.csv b/experiments/tuning/results/incode/lucene.csv new file mode 100644 index 0000000..3b41f38 --- /dev/null +++ b/experiments/tuning/results/incode/lucene.csv @@ -0,0 +1,126 @@ +ATFD;LAA;FDP;F-measure +2;3;3;0.5189189189189188 +3;3;3;0.5185185185185185 +3;3;4;0.5110410094637224 +2;3;4;0.5050505050505051 +3;3;5;0.4954128440366972 +2;3;5;0.48899755501222497 +3;2;3;0.47384615384615386 +3;2;4;0.4576271186440678 +1;4;3;0.45317220543806647 +2;4;3;0.45317220543806647 +2;4;4;0.44827586206896547 +1;4;4;0.44827586206896547 +3;2;5;0.4402173913043478 +4;3;4;0.44015444015444016 +3;4;4;0.43866171003717475 +1;4;5;0.4369747899159664 +2;4;5;0.4369747899159664 +3;4;3;0.43410852713178294 +4;3;5;0.4318181818181818 +2;3;2;0.43037974683544306 +3;4;5;0.4290909090909091 +4;3;3;0.424 +2;2;3;0.4232365145228216 +4;4;4;0.42105263157894735 +4;4;5;0.41434262948207173 +3;3;2;0.41064638783269963 +2;5;3;0.4063492063492063 +1;5;3;0.4063492063492063 +4;4;3;0.4049586776859504 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+5;2;1;0.11347517730496452 +5;1;1;0.1095890410958904 diff --git a/experiments/tuning/results/smad/god_class/lucene.csv b/experiments/tuning/results/smad/god_class/lucene.csv new file mode 100644 index 0000000..aff6527 --- /dev/null +++ b/experiments/tuning/results/smad/god_class/lucene.csv @@ -0,0 +1,27 @@ +Learning rate;Beta;Dense sizes;F-measure +0.393711052749;0.0432050877649;[62];0.4579715366600612 +0.0751704870925;0.00698905045764;[100, 32, 16];0.456078850913108 +0.305061691006;0.166384422536;[56, 30];0.4551765430581341 +0.0472565088738;0.00880802437069;[94, 80];0.4511419952596423 +0.160357707956;0.00587158826125;[14, 4];0.4504064311756619 +0.25741042025;0.0142762914137;[84, 65];0.4336820403471508 +0.328759349571;0.0140718393725;[98, 24, 10];0.43116883116883115 +0.675222409315;0.0202395020189;[60, 46, 29];0.430787037037037 +0.487578661973;0.126117872652;[54, 37, 11];0.43052983568315134 +0.71126623341;0.0166993028532;[38, 34];0.428066584626496 +0.213053719883;0.0348642470258;[98, 46, 17];0.4274454473147284 +0.0135967563988;0.564863630516;[66, 25];0.14079348844941633 +0.00938117431022;0.0193405524182;[98, 20];0.12372909110713605 +0.0108254255976;0.00731617254468;[97, 47, 17];0.10866250338046313 +0.00831031109157;0.258329630389;[61];0.09876676351469339 +0.0179371081537;0.0138059593127;[35, 7];0.09641238744292178 +0.00924350691385;0.84560782437;[23, 21, 12];0.0873653001917447 +0.00321548138533;0.00986752623029;[49, 48];0.07330373596567194 +0.0300880351016;0.0198979824928;[30, 12, 5];0.07180004635560645 +0.00690952666076;0.0301226190824;[41, 30];0.056063816563107494 +0.00458297285531;0.114666403144;[25];0.0538359388896302 +0.00576335156043;0.091376326273;[10, 9];0.050343512027123595 +0.0157090707474;0.0282725885728;[7];0.04638522903399187 +0.00487700095575;0.0499278064708;[19];0.03636805022718979 +0.787943964179;0.503380835549;[81, 42, 21];0.030913978494623653 +0.233693105715;0.681569442872;[83];0.030913978494623653 diff --git a/experiments/tuning/tune_asci.py b/experiments/tuning/tune_asci.py index 3ecde9e..964d69f 100644 --- a/experiments/tuning/tune_asci.py +++ b/experiments/tuning/tune_asci.py @@ -6,6 +6,7 @@ import argparse import os +import random training_systems = { 'android-frameworks-opt-telephony', @@ -23,7 +24,7 @@ def parse_args(): parser.add_argument("antipattern", help="Either 'god_class' or 'feature_envy'.") parser.add_argument("test_system", help="The name of the system to be used for testing.\n Hence, the training will be performed using all the systems except this one.") parser.add_argument("-n_fold", type=int, default=5, help="Number of folds (k) for a k-fold-cross-validation") - parser.add_argument("-n_test", type=int, default=100, help="Number of random hyper-parameters sets to be tested") + parser.add_argument("-n_test", type=int, default=200, help="Number of random hyper-parameters sets to be tested") return parser.parse_args() # Build the dataset for asci, i.e., the labels are the indexes of the best tool for each input instance. @@ -33,7 +34,7 @@ def parse_args(): # idx = 2: JDeodorant def build_asci_dataset(antipattern, systems): # Get real instances and labels - instances, labels = nnUtils.build_dataset(antipattern, systems) + instances, labels = nnUtils.build_dataset(antipattern, systems, True) # Compute the performances of each tool in order to sort them accordingly nb_tools = 3 @@ -58,36 +59,58 @@ def build_asci_dataset(antipattern, systems): if toolsOverallPredictions[toolIndex][i] == label: toolsIndexes[i] = toolIndex - return instances, np.array(toolsIndexes) + return instances, np.reshape(np.array(toolsIndexes), (len(toolsIndexes), 1)) + +def generateRandomHyperparameters(): + max_features = random.choice(['sqrt', 'log2', None]) + max_depth = random.choice([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None]) + min_samples_leaf = random.choice([1, 2, 3, 4, 5]) + min_samples_split = 10**-random.uniform(1.0, 3.0) + + return max_features, max_depth, min_samples_leaf, min_samples_split if __name__ == "__main__": args = parse_args() # Remove the test system from the training set and build dataset training_systems.remove(args.test_system) - data_x, data_y = build_asci_dataset(args.antipattern, training_systems) - random_grid = { - 'max_features': ['sqrt', 'log2', None], - 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None], - 'min_samples_leaf': [1, 2, 4, 6], - 'min_samples_split': [2, 5, 10, 15]} + # Build ASCI training dataset: instances and asci labels (i.e., tools indexes) + dataset_x, dataset_y = build_asci_dataset(args.antipattern, training_systems) + + params = [] + perfs = np.zeros((args.n_test, 3)) + for i in range(args.n_test): + max_features, max_depth, min_samples_leaf, min_samples_split = generateRandomHyperparameters() + params.append([max_features, max_depth, min_samples_leaf, min_samples_split]) + + # Due to the randomness of the process, repeat the cross validation 3 times + # per hyper-pameters' set and take the average performance value. + for j in range(3): + data_x, data_y = nnUtils.shuffle(dataset_x, dataset_y) + predictions = np.empty(shape=[0, 1]) + for k in range(args.n_fold): + # Create the training and testing datasets for this fold + x_train, y_train, x_test, y_test = nnUtils.get_cross_validation_dataset(data_x, data_y, k, args.n_fold) + + clf = tree.DecisionTreeClassifier( + max_features=max_features, + max_depth=max_depth, + min_samples_leaf=min_samples_leaf, + min_samples_split=min_samples_split) + + clf = clf.fit(x_train, y_train) - cross_validation = RandomizedSearchCV( - estimator = tree.DecisionTreeClassifier(), - param_distributions = random_grid, - n_iter = args.n_test, - cv = args.n_fold, - verbose=2, - n_jobs = -1) + predictions = np.concatenate((predictions, np.reshape(clf.predict(x_test), (len(x_test), 1))), axis=0) + perfs[i, j] = nnUtils.accuracy(predictions, data_y) - cross_validation.fit(data_x, data_y) - best_params = cross_validation.best_params_ + perfs = np.mean(perfs, axis=1) + indexes = np.argsort(perfs) tuning_results_file = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'asci', args.antipattern, args.test_system + '.csv') with open(tuning_results_file, 'w') as file: - for key in best_params: - file.write(str(key) + ';') - file.write('\n') - for key in best_params: - file.write(str(best_params[key]) + ';') \ No newline at end of file + file.write("Max features;Max depth;Min samples leaf;Min samples split;Accuracy\n") + for i in reversed(indexes): + for j in range(len(params[i])): + file.write(str(params[i][j]) + ';') + file.write(str(perfs[i]) + '\n') diff --git a/experiments/tuning/tune_hist.py b/experiments/tuning/tune_hist.py new file mode 100644 index 0000000..0bd1a26 --- /dev/null +++ b/experiments/tuning/tune_hist.py @@ -0,0 +1,64 @@ +from context import ROOT_DIR, nnUtils, hist_gc, hist_fe + +import numpy as np + +import argparse +import os +import progressbar + +systems = { + 'android-frameworks-opt-telephony', + 'android-platform-support', + 'apache-ant', + 'lucene', + 'apache-tomcat', + 'argouml', + 'jedit', + 'xerces-2_7_0' +} + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("antipattern", help="Either 'god_class' or 'feature_envy'") + parser.add_argument("test_system", help="The name of the system to be used for testing.\n Hence, the cross-validation will be performed using all the systems except this one.") + return parser.parse_args() + +if __name__ == '__main__': + args = parse_args() + + # Remove the test system from the training set and build dataset + systems.remove(args.test_system) + systems = list(systems) + + # Get overall labels + overall_labels = np.empty(shape=[0, 1]) + for system in systems: + overall_labels = np.concatenate((overall_labels, nnUtils.getLabels(args.antipattern, system)), axis=0) + + params = np.arange(0.0, 20.0, 0.5) if args.antipattern == 'god_class' else np.arange(1.0, 3.0, 0.1) + hist_detect = hist_gc.detect_with_params if args.antipattern == 'god_class' else hist_fe.detect_with_params + + # Initialize progressbar + bar = progressbar.ProgressBar(maxval=len(params), \ + widgets=['Tuning HIST parameters for ' + args.test_system + ': ' ,progressbar.Percentage()]) + bar.start() + + perfs = [] + count = 0 + for alpha in params: + count += 1 + bar.update(count) + overall_prediction = np.empty(shape=[0, 1]) + for system in systems: + prediction = nnUtils.predictFromDetect(args.antipattern, system, hist_detect(system, alpha)) + overall_prediction = np.concatenate((overall_prediction, prediction), axis=0) + perfs.append(nnUtils.f_measure(overall_prediction, overall_labels)) + bar.finish() + + output_file_path = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'hist', args.antipattern, args.test_system + '.csv') + + indexes = np.argsort(np.array(perfs)) + with open(output_file_path, 'w') as file: + file.write("Alpha;F-measure\n") + for i in reversed(indexes): + file.write(str(params[i]) + ';' + str(perfs[i]) + '\n') \ No newline at end of file diff --git a/experiments/tuning/tune_incode.py b/experiments/tuning/tune_incode.py new file mode 100644 index 0000000..db80d91 --- /dev/null +++ b/experiments/tuning/tune_incode.py @@ -0,0 +1,64 @@ +from context import ROOT_DIR, nnUtils, incode + +import numpy as np + +import argparse +import os +import progressbar + +systems = { + 'android-frameworks-opt-telephony', + 'android-platform-support', + 'apache-ant', + 'lucene', + 'apache-tomcat', + 'argouml', + 'jedit', + 'xerces-2_7_0' +} + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("test_system", help="The name of the system to be used for testing.\n Hence, the training will be performed using all the systems except this one.") + return parser.parse_args() + +if __name__ == '__main__': + args = parse_args() + + # Remove the test system from the training set and build dataset + systems.remove(args.test_system) + systems = list(systems) + + # Get overall labels + overall_labels = np.empty(shape=[0, 1]) + for system in systems: + overall_labels = np.concatenate((overall_labels, nnUtils.getLabels('feature_envy', system)), axis=0) + + params = [(atfd, laa, fdp) for atfd in range(1, 6) for laa in range(1, 6) for fdp in range(1, 6)] + + # Initialize progressbar + bar = progressbar.ProgressBar(maxval=len(params), \ + widgets=['Tuning InCode parameters for ' + args.test_system + ': ' ,progressbar.Percentage()]) + bar.start() + + perfs = [] + count = 0 + for atfd, laa, fdp in params: + count += 1 + bar.update(count) + overall_prediction = np.empty(shape=[0, 1]) + for system in systems: + prediction = nnUtils.predictFromDetect('feature_envy', system, incode.detect_with_params(system, atfd, laa, fdp)) + overall_prediction = np.concatenate((overall_prediction, prediction), axis=0) + perfs.append(nnUtils.f_measure(overall_prediction, overall_labels)) + bar.finish() + + output_file_path = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'incode', args.test_system + '.csv') + + indexes = np.argsort(np.array(perfs)) + with open(output_file_path, 'w') as file: + file.write("ATFD;LAA;FDP;F-measure\n") + for i in reversed(indexes): + atfd, laa, fdp = params[i] + file.write(str(atfd) + ';' + str(laa) + ';' + str(fdp) + ';') + file.write(str(perfs[i]) + '\n') diff --git a/experiments/tuning/tune_smad.py b/experiments/tuning/tune_smad.py index 6c014a2..14ce6db 100644 --- a/experiments/tuning/tune_smad.py +++ b/experiments/tuning/tune_smad.py @@ -44,25 +44,6 @@ def generateRandomHyperParameters(): return learning_rate, beta, dense_sizes -# Returns a training and a testing dataset from an input dataset (instances and labels) -# The input dataset is first split into n_folds folds. -# The test dataset is the fold of index fold_index -# The training dataset is obtained by concatenating the n_folds-1 remaining folds. -# X : instances -# Y : labels -# fold_index: the index of the fold we want to be returned as the test dataset -# n_fold : the number of folds, i.e., k for a k-fold cross-validation -def get_cross_validation_dataset(X, Y, fold_index, n_fold): - folds_x, folds_y = nnUtils.split(X, Y, n_fold) - x_train = np.empty(shape=[0, X.shape[-1]]) - y_train = np.empty(shape=[0, 1]) - for i in range(n_fold): - if i != fold_index: - x_train = np.concatenate((x_train, folds_x[i]), axis=0) - y_train = np.concatenate((y_train, folds_y[i]), axis=0) - - return x_train, y_train, folds_x[fold_index], folds_y[fold_index] - def train(session, model, x_train, y_train, num_step, lr, beta): for step in range(num_step): feed_dict_train = { @@ -78,8 +59,7 @@ def train(session, model, x_train, y_train, num_step, lr, beta): # Remove the test system from the training set and build dataset training_systems.remove(args.test_system) - data_x, data_y = nnUtils.build_dataset(args.antipattern, training_systems) - data_x, data_y = nnUtils.shuffle(data_x, data_y) + dataset_x, dataset_y = nnUtils.build_dataset(args.antipattern, training_systems) bar = progressbar.ProgressBar(maxval=args.n_test, \ widgets=['Performing cross validation for ' + args.test_system + ': ' ,progressbar.Percentage()]) @@ -93,36 +73,43 @@ def train(session, model, x_train, y_train, num_step, lr, beta): learning_rate, beta, dense_sizes = generateRandomHyperParameters() params.append([learning_rate, beta, dense_sizes]) - predictions = np.empty(shape=[0, 1]) - for j in range(args.n_fold): - # Create the training and testing datasets for this fold - x_train, y_train, x_test, y_test = get_cross_validation_dataset(data_x, data_y, j, args.n_fold) - - # New graph - tf.reset_default_graph() - - # Create model - model = md.SMAD( - shape=dense_sizes, - input_size=x_train.shape[-1]) - - with tf.Session() as session: - # Initialize the variables of the TensorFlow graph. - session.run(tf.global_variables_initializer()) - - # Train the model - train( - session=session, - model=model, - x_train=x_train, - y_train=y_train, - num_step=args.n_step, - lr=learning_rate, - beta=beta) - - predictions = np.concatenate((predictions, session.run(model.inference, feed_dict={model.input_x: x_test})), axis=0) - - perfs.append(nnUtils.f_measure(predictions, data_y)) + # Due to the randomness of the process, repeat the cross validation 3 times + # per hyper-pameters' set and take the average performance value. + performances = [] + for j in range(3): + data_x, data_y = nnUtils.shuffle(dataset_x, dataset_y) + + predictions = np.empty(shape=[0, 1]) + for k in range(args.n_fold): + # Create the training and testing datasets for this fold + x_train, y_train, x_test, y_test = nnUtils.get_cross_validation_dataset(data_x, data_y, k, args.n_fold) + + # New graph + tf.reset_default_graph() + + # Create model + model = md.SMAD( + shape=dense_sizes, + input_size=x_train.shape[-1]) + + with tf.Session() as session: + # Initialize the variables of the TensorFlow graph. + session.run(tf.global_variables_initializer()) + + # Train the model + train( + session=session, + model=model, + x_train=x_train, + y_train=y_train, + num_step=args.n_step, + lr=learning_rate, + beta=beta) + + predictions = np.concatenate((predictions, session.run(model.inference, feed_dict={model.input_x: x_test})), axis=0) + performances.append(nnUtils.f_measure(predictions, data_y)) + perfs.append(np.mean(np.array(performances), axis=0)) + indexes = np.argsort(np.array(perfs)) with open(output_file_path, 'w') as file: file.write("Learning rate;Beta;Dense sizes;F-measure\n") diff --git a/neural_networks/asci/context.py b/neural_networks/asci/context.py index def04de..9cd0240 100644 --- a/neural_networks/asci/context.py +++ b/neural_networks/asci/context.py @@ -4,12 +4,12 @@ ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')) sys.path.insert(0, ROOT_DIR) -import detection_tools.god_class.hist as hist_gc -import detection_tools.god_class.decor as decor_gc -import detection_tools.god_class.jdeodorant as jdeodorant_gc +import neural_networks.decor.detect as decor +import neural_networks.hist.detect_god_class as hist_gc +import neural_networks.jdeodorant.detect_god_class as jdeodorant_gc -import detection_tools.feature_envy.hist as hist_fe -import detection_tools.feature_envy.incode as incode_fe -import detection_tools.feature_envy.jdeodorant as jdeodorant_fe +import neural_networks.incode.detect as incode +import neural_networks.hist.detect_feature_envy as hist_fe +import neural_networks.jdeodorant.detect_feature_envy as jdeodorant_fe import utils.nnUtils as nnUtils \ No newline at end of file diff --git a/neural_networks/asci/predict.py b/neural_networks/asci/predict.py index 060793e..2dfd61c 100644 --- a/neural_networks/asci/predict.py +++ b/neural_networks/asci/predict.py @@ -1,4 +1,4 @@ -from context import nnUtils, decor_gc, incode_fe, hist_gc, hist_fe, jdeodorant_gc, jdeodorant_fe +from context import nnUtils, decor, incode, hist_gc, hist_fe, jdeodorant_gc, jdeodorant_fe from sklearn import tree import numpy as np @@ -9,18 +9,20 @@ def getToolsPredictions(antipattern, system): assert antipattern in ['god_class', 'feature_envy'] if antipattern == 'god_class': - return [decor_gc.predict(system), hist_gc.predict(system), jdeodorant_gc.predict(system)] + toolsOutputs = [decor.detect(system), hist_gc.detect(system), jdeodorant_gc.detect(system)] else: - return [incode_fe.predict(system), hist_fe.predict(system), jdeodorant_fe.predict(system)] + toolsOutputs = [incode.detect(system), hist_fe.detect(system), jdeodorant_fe.detect(system)] + + return map(lambda x: nnUtils.predictFromDetect(antipattern, system, x), toolsOutputs) def predict(antipattern, system): toolsPredictions = getToolsPredictions(antipattern, system) - X = nnUtils.getInstances(antipattern, system) + X = nnUtils.getInstances(antipattern, system, True) - # Ensemble Prediction + # Compute ensemble prediction over 10 pre-trained classifiers predictions = np.zeros((10, X.shape[0], 1)) for i in range(10): - with open(nnUtils.get_save_path(i), 'r') as file: + with open(nnUtils.get_save_path('asci', antipattern, system, i), 'r') as file: clf = pickle.load(file) predictedToolIndexes = clf.predict(X) for j, toolIndex in enumerate(predictedToolIndexes): diff --git a/neural_networks/asci/trained_models/.DS_Store b/neural_networks/asci/trained_models/.DS_Store index acc36ce79bdc6bc75c8bd39d753e1910908cad3f..6bb46638c0bd0e56ff1a0b7ffd4168d66d30c099 100644 GIT binary patch delta 111 zcmZoMXfc=|&e%S&P;8=}q9_vs0|O%ig8&0VDMJxMDnmRF=S|$GKG{HoMUjUg4aiMo zC_$EFC}XHhDNfEw%FoZ4*e%bXUK5>Km#*1d`6CbE= dX6F##U~JoX@jLTmei2<(kd6bJZAA_<0|5EC6*B+; diff --git a/neural_networks/asci/trained_models/feature_envy/.DS_Store b/neural_networks/asci/trained_models/feature_envy/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ 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-1,11 +1,18 @@ from __future__ import division -from context import dataUtils, entityUtils +from context import ROOT_DIR, dataUtils, entityUtils, nnUtils import numpy as np import progressbar +import os -def detect(systemName, alpha=2.6): +def detect(systemName): + tuning_file = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'hist', 'feature_envy', systemName + '.csv') + + params = nnUtils.get_optimal_hyperparameters(tuning_file) + return detect_with_params(systemName, params['Alpha']) + +def detect_with_params(systemName, alpha): # Get and prepare all data needed (methods, classes, history) methods = dataUtils.getMethods(systemName) methodToIndexMap = {m: i for i, m in enumerate(methods)} diff --git a/neural_networks/hist/detect_god_class.py b/neural_networks/hist/detect_god_class.py index 13c5420..d4aea2a 100644 --- a/neural_networks/hist/detect_god_class.py +++ b/neural_networks/hist/detect_god_class.py @@ -1,15 +1,23 @@ from __future__ import division -from context import dataUtils +from context import ROOT_DIR, dataUtils, nnUtils import numpy as np -def detect(systemName, alpha=8.0): +import os + +def detect(systemName): + tuning_file = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'hist', 'god_class', systemName + '.csv') + + params = nnUtils.get_optimal_hyperparameters(tuning_file) + return detect_with_params(systemName, params['Alpha']) + + +def detect_with_params(systemName, alpha): # Get and prepare all data needed (classes, history) classes = dataUtils.getClasses(systemName) classToIndexMap = {klass: i for i, klass in enumerate(classes)} history = dataUtils.getHistory(systemName, "C") - # Compute for each class, the number of commit involving at least another class, # and the number of occurences in this set of commit. diff --git a/neural_networks/incode/context.py b/neural_networks/incode/context.py index ad333f1..e5e600f 100644 --- a/neural_networks/incode/context.py +++ b/neural_networks/incode/context.py @@ -5,4 +5,5 @@ sys.path.insert(0, ROOT_DIR) import utils.dataUtils as dataUtils -import utils.entityUtils as entityUtils \ No newline at end of file +import utils.entityUtils as entityUtils +import utils.nnUtils as nnUtils \ No newline at end of file diff --git a/neural_networks/incode/detect.py b/neural_networks/incode/detect.py index 0695fc3..16bd0aa 100644 --- a/neural_networks/incode/detect.py +++ b/neural_networks/incode/detect.py @@ -1,11 +1,16 @@ from __future__ import division -from context import ROOT_DIR, dataUtils, entityUtils +from context import ROOT_DIR, dataUtils, entityUtils, nnUtils import csv import os +def detect(systemName): + tuning_file = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'incode', systemName + '.csv') -def detect(systemName, atfd=2.0, laa=3.0 , fdp=3.0): + params = nnUtils.get_optimal_hyperparameters(tuning_file) + return detect_with_params(systemName, params['ATFD'], params['LAA'], params['FDP']) + +def detect_with_params(systemName, atfd, laa, fdp): incodeMetricsFile = os.path.join(ROOT_DIR, 'data/metric_files/incode/' + systemName + '.csv') classes = dataUtils.getAllClasses(systemName) diff --git a/neural_networks/smad/predict.py b/neural_networks/smad/predict.py index 0a0c703..4c28c53 100644 --- a/neural_networks/smad/predict.py +++ b/neural_networks/smad/predict.py @@ -6,18 +6,10 @@ import csv import os -def get_optimal_parameters(antipattern, system): - tuning_file = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'smad', antipattern, test_system + '.csv') - - with open(tuning_file, 'r') as file: - reader = csv.DictReader(file, delimiter=';') - - for row in reader: - if row['F-measure'] != 'nan': - return {key:ast.literal_eval(row[key]) for key in row} - def predict(antipattern, system): - params = get_optimal_parameters(antipattern, system) + tuning_file = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'smad', antipattern, system + '.csv') + + params = nnUtils.get_optimal_hyperparameters(tuning_file) X = nnUtils.getInstances(antipattern, system) # New graph diff --git a/neural_networks/smad/trained_models/god_class/jedit/checkpoint b/neural_networks/smad/trained_models/god_class/jedit/checkpoint deleted file mode 100644 index 757faa5..0000000 --- a/neural_networks/smad/trained_models/god_class/jedit/checkpoint +++ /dev/null @@ -1,11 +0,0 @@ -model_checkpoint_path: "/Users/antoinebarbez/Desktop/tensorflow/SMAD/neural_networks/smad/trained_models/god_class/jedit/model_9" -all_model_checkpoint_paths: "/Users/antoinebarbez/Desktop/tensorflow/SMAD/neural_networks/smad/trained_models/god_class/jedit/model_0" -all_model_checkpoint_paths: "/Users/antoinebarbez/Desktop/tensorflow/SMAD/neural_networks/smad/trained_models/god_class/jedit/model_1" -all_model_checkpoint_paths: "/Users/antoinebarbez/Desktop/tensorflow/SMAD/neural_networks/smad/trained_models/god_class/jedit/model_2" -all_model_checkpoint_paths: 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import random @@ -32,15 +34,21 @@ def f_measure(output, labels): return 2*p*r/(p+r) +def accuracy(output, labels): + true = np.sum((output == labels).astype(float)) + size = len(output) + + return true/size + ### UTILS ### -def build_dataset(antipattern, systems): - input_size = {'god_class':8, 'feature_envy':9} +def build_dataset(antipattern, systems, normalized=True): + input_size = {'god_class':6, 'feature_envy':7} X = np.empty(shape=[0, input_size[antipattern]]) Y = np.empty(shape=[0, 1]) for systemName in systems: - X = np.concatenate((X, getInstances(antipattern, systemName)), axis=0) + X = np.concatenate((X, getInstances(antipattern, systemName, normalized)), axis=0) Y = np.concatenate((Y, getLabels(antipattern, systemName)), axis=0) return X, Y @@ -57,6 +65,33 @@ def ensemble_prediction(model, save_paths, input_x): return np.mean(np.array(predictions), axis=0) +# Returns a training and a testing dataset from an input dataset (instances and labels) +# The input dataset is first split into n_folds folds. +# The test dataset is the fold of index fold_index +# The training dataset is obtained by concatenating the n_folds-1 remaining folds. +# X : instances +# Y : labels +# fold_index: the index of the fold we want to be returned as the test dataset +# n_fold : the number of folds, i.e., k for a k-fold cross-validation +def get_cross_validation_dataset(X, Y, fold_index, n_fold): + folds_x, folds_y = split(X, Y, n_fold) + x_train = np.empty(shape=[0, X.shape[-1]]) + y_train = np.empty(shape=[0, 1]) + for i in range(n_fold): + if i != fold_index: + x_train = np.concatenate((x_train, folds_x[i]), axis=0) + y_train = np.concatenate((y_train, folds_y[i]), axis=0) + + return x_train, y_train, folds_x[fold_index], folds_y[fold_index] + +def get_optimal_hyperparameters(tuning_file): + with open(tuning_file, 'r') as file: + reader = csv.DictReader(file, delimiter=';') + + for row in reader: + if row['F-measure'] != 'nan': + return {key:ast.literal_eval(row[key]) for key in row} + # Get the path of a trained model for a given approach (smad or asci) def get_save_path(approach, antipattern, test_system, model_number): directory = os.path.join(ROOT_DIR, 'neural_networks', approach, 'trained_models', antipattern, test_system) @@ -106,16 +141,19 @@ def predictFromDetect(antipattern, systemName, smells): return np.array(prediction) -def shuffle(X, Y): - assert len(X) == len(Y), 'X and Y must have the same number of elements' +# Shuffle identically several arrays +def shuffle(X, *args): + for arg in args: + assert len(X) == len(arg), 'all arrays to be shuffled must have the same number of elements' idx = range(len(X)) random.shuffle(idx) - shuffled_X = np.array([X[i] for i in idx]) - shuffled_Y = np.array([Y[i] for i in idx]) + output = [np.array([X[i] for i in idx])] + for arg in args: + output.append(np.array([arg[i] for i in idx])) - return shuffled_X, shuffled_Y + return tuple(output) def split(X, Y, nb_split): assert len(X) == len(Y), 'X and Y must have the same number of elements' @@ -168,16 +206,16 @@ def getInstances(antipattern, systemName, normalized=True): instances = np.array(instances).astype(float) # Batch normalization - '''if normalized: + if normalized: scaler = StandardScaler() scaler.fit(instances) - return scaler.transform(instances)''' + instances = scaler.transform(instances) - scaler = StandardScaler() + '''scaler = StandardScaler() scaler.fit(instances) - instances = scaler.transform(instances) + instances = scaler.transform(instances)''' - instances = np.concatenate((instances, np.tile(getSystemConstants(systemName), (instances.shape[0],1))), axis=1) + #instances = np.concatenate((instances, np.tile(getSystemConstants(systemName), (instances.shape[0],1))), axis=1) return instances