bug-2022: part 3 done

bug-2022/.gitignore

@@ -1,3 +1,5 @@
 defects4j/
 sources/
 env/
+*.iml
+.idea/

bug-2022/extract_feature_vectors.py

@@ -24,13 +24,14 @@ def count(node: javalang.tree.Node, the_filter) -> int:
 
 
 def metrics(clazz: javalang.tree.ClassDeclaration) -> dict[str, str]:
-    m = {}
+    # Method metrics
-
+    m = {
-    m['MTH'] = len(clazz.methods)  # MTH: #Methods
+        'MTH': len(clazz.methods),
-    m['FLD'] = len(clazz.fields)  # FLD: #Fields
+        'FLD': len(clazz.fields),
+        'RFC': 0
+    }
 
     # RFC: #Public methods + #Method invocations
-    m['RFC'] = 0
     for method in clazz.methods:
         if 'public' in method.modifiers:
             m['RFC'] += 1
@@ -81,8 +82,8 @@ def metrics(clazz: javalang.tree.ClassDeclaration) -> dict[str, str]:
             # BCM: #block comments
             m['BCM'] += 1
 
-            # #Words (longest alphanumeric substrings) in block comments
+            # #Words (the longest alphanumeric substrings) in block comments
-            m['WRD'] += len(re.findall('\w+', node.documentation))
+            m['WRD'] += len(re.findall('\\w+', node.documentation))
 
     # #Words in comments / #Statements
     s = count(clazz, javalang.tree.Statement)

bug-2022/label_feature_vectors.py

@@ -1,10 +1,11 @@
 #!/usr/bin/env python3
-import javalang
-import os
-import pandas as pd
 import glob
+import os
 import re
 
+import javalang
+import pandas as pd
+
 # God class if:
 # |M(C)| > E(M) + 6*V(M)
 # (number of methods greater than average across all classes plus 6 times the
@@ -22,24 +23,20 @@ def clean_output():
         os.remove(f)
 
 
-def get_is_buggy_df() -> pd.DataFrame:
+def get_is_buggy_set() -> set[str]:
-    lines: list[str] = set()
+    lines: set[str] = set()
 
     filelist = glob.glob(LABEL_DIR + '/*.src')
     for f in filelist:
-        with open(f, 'r') as fobj:
+        with open(f, 'r') as file_obj:
-            for line in fobj.readlines():
+            for line in file_obj.readlines():
                 lines.add(line.strip().strip('\n'))
 
     return lines
 
 
-def is_class_buggy(fqdn: str, se) -> bool:
-    clazz_path = fqdn.replace('.', '/')
-
-
 def main():
-    is_buggy_set = get_is_buggy_df()
+    is_buggy_set = get_is_buggy_set()
     df = pd.read_csv(IN_DIR + '/feature_vectors.csv')
     df['buggy'] = df['class_name'].apply(
         lambda fqdn: 1 if fqdn in is_buggy_set else 0)

bug-2022/models/models.csv

@@ -0,0 +1,130 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,params,split0_test_precision,split1_test_precision,split2_test_precision,split3_test_precision,split4_test_precision,mean_test_precision,std_test_precision,rank_test_precision,split0_test_recall,split1_test_recall,split2_test_recall,split3_test_recall,split4_test_recall,mean_test_recall,std_test_recall,rank_test_recall,split0_test_f1,split1_test_f1,split2_test_f1,split3_test_f1,split4_test_f1,mean_test_f1,std_test_f1,rank_test_f1,classifier,param_gamma,param_kernel,param_criterion,param_splitter,param_class_weight,param_max_features,param_activation,param_hidden_layer_sizes,param_learning_rate,param_max_iter,param_solver
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+0.08247060775756836,0.06628108340985492,0.0029076099395751952,4.565615565741273e-05,"{'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'sgd'}",0.09027777777777778,0.058394160583941604,0.10067114093959731,0.0,0.0603448275862069,0.061937581377504713,0.03508507389421089,103,0.8666666666666667,0.5333333333333333,1.0,0.0,0.4666666666666667,0.5733333333333334,0.3492213560989012,6,0.16352201257861634,0.10526315789473684,0.18292682926829268,0.0,0.10687022900763361,0.11171644574985588,0.06371880486501004,101,MLPClassifier,,,,,,,logistic,"(10,)",invscaling,2000,sgd
+0.32681841850280763,0.03737471096500944,0.003072023391723633,0.00021893857520252248,"{'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'adam'}",0.0,0.0,0.6666666666666666,0.5714285714285714,0.75,0.39761904761904765,0.32953619574849435,86,0.0,0.0,0.13333333333333333,0.26666666666666666,0.2,0.12000000000000002,0.10666666666666666,99,0.0,0.0,0.2222222222222222,0.36363636363636365,0.31578947368421056,0.18032961190855928,0.15410598174136725,92,MLPClassifier,,,,,,,logistic,"(10,)",invscaling,2000,adam
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+0.37090563774108887,0.02666214108353507,0.0027956485748291014,1.3399378682498446e-05,"{'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'sgd'}",0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,MLPClassifier,,,,,,,logistic,"(10,)",adaptive,2000,sgd
+0.2881649971008301,0.023747046755495084,0.0028429508209228517,7.194608417777043e-05,"{'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'adam'}",0.0,0.0,0.7142857142857143,1.0,0.0,0.34285714285714286,0.4295227536677974,92,0.0,0.0,0.3333333333333333,0.06666666666666667,0.0,0.07999999999999999,0.12927146286443542,102,0.0,0.0,0.4545454545454545,0.125,0.0,0.11590909090909089,0.17610336934373194,100,MLPClassifier,,,,,,,logistic,"(10,)",adaptive,2000,adam
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+0.05492725372314453,0.0017188900888596406,0.0031635284423828123,3.219530197389177e-05,"{'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'lbfgs'}",0.4,0.3684210526315789,0.5,0.4166666666666667,0.38461538461538464,0.41394062078272603,0.04591293836418872,80,0.4,0.4666666666666667,0.6,0.3333333333333333,0.3333333333333333,0.42666666666666664,0.09977753031397177,31,0.4000000000000001,0.4117647058823529,0.5454545454545454,0.3703703703703704,0.3571428571428571,0.4169464957700252,0.0671925057567224,49,MLPClassifier,,,,,,,logistic,"(50,)",invscaling,2000,lbfgs
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+0.8062046051025391,0.09699132694897834,0.0033336162567138674,8.535217304817459e-05,"{'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'sgd'}",0.5,0.0,1.0,0.0,0.75,0.45,0.4,67,0.06666666666666667,0.0,0.06666666666666667,0.0,0.2,0.06666666666666668,0.07302967433402216,103,0.11764705882352941,0.0,0.125,0.0,0.31578947368421056,0.11168730650154798,0.1156015138064585,102,MLPClassifier,,,,,,,logistic,"(50,)",adaptive,2000,sgd
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+0.3580580234527588,0.06301164433652041,0.003572416305541992,0.0001459300429175471,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'lbfgs'}",0.3888888888888889,0.3,0.3684210526315789,0.5,0.5454545454545454,0.4205528973950027,0.08962767825148864,78,0.4666666666666667,0.4,0.4666666666666667,0.4666666666666667,0.4,0.44000000000000006,0.03265986323710904,24,0.42424242424242425,0.34285714285714286,0.4117647058823529,0.4827586206896552,0.4615384615384615,0.4246322710420074,0.048153166507795296,40,MLPClassifier,,,,,,,logistic,"(100, 20)",constant,2000,lbfgs
+0.2549830436706543,0.020565032362827355,0.003612184524536133,0.00011226681029727733,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'sgd'}",0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,MLPClassifier,,,,,,,logistic,"(100, 20)",constant,2000,sgd
+1.1718737602233886,0.24094826769673328,0.003545999526977539,0.00011167349109499017,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'adam'}",0.6,0.6363636363636364,0.5833333333333334,0.55,0.3333333333333333,0.5406060606060608,0.10730610677386535,47,0.4,0.4666666666666667,0.4666666666666667,0.7333333333333333,0.2,0.4533333333333334,0.17074997966487596,16,0.48,0.5384615384615385,0.5185185185185186,0.6285714285714286,0.25,0.4831102971102972,0.12635388984541385,6,MLPClassifier,,,,,,,logistic,"(100, 20)",constant,2000,adam
+0.3377342700958252,0.07612657036083671,0.0035915374755859375,6.52516414274953e-05,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'lbfgs'}",0.5625,0.375,0.3333333333333333,0.47368421052631576,0.3,0.40890350877192977,0.09644552346591491,81,0.6,0.4,0.4,0.6,0.2,0.44000000000000006,0.14966629547095764,24,0.5806451612903225,0.38709677419354843,0.3636363636363636,0.5294117647058824,0.24,0.42015801276522335,0.12201406397172566,47,MLPClassifier,,,,,,,logistic,"(100, 20)",invscaling,2000,lbfgs
+0.29044718742370607,0.234180550229874,0.0035352230072021483,0.0001671404638852201,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'sgd'}",0.0,0.0,0.10067114093959731,0.0,0.0,0.020134228187919462,0.040268456375838924,104,0.0,0.0,1.0,0.0,0.0,0.2,0.4000000000000001,97,0.0,0.0,0.18292682926829268,0.0,0.0,0.036585365853658534,0.07317073170731707,103,MLPClassifier,,,,,,,logistic,"(100, 20)",invscaling,2000,sgd
+1.1548531532287598,0.2776065482281074,0.0036094188690185547,0.00011349165130518803,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'adam'}",0.5,0.5833333333333334,0.5833333333333334,0.4444444444444444,0.3333333333333333,0.48888888888888904,0.09395296958493204,57,0.3333333333333333,0.4666666666666667,0.4666666666666667,0.5333333333333333,0.2,0.39999999999999997,0.11925695879998878,46,0.4,0.5185185185185186,0.5185185185185186,0.4848484848484848,0.25,0.4343771043771044,0.10186620373603875,34,MLPClassifier,,,,,,,logistic,"(100, 20)",invscaling,2000,adam
+0.3444072246551514,0.09869928584054863,0.003534078598022461,0.0001186712520928932,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'lbfgs'}",0.4166666666666667,0.4166666666666667,0.6666666666666666,0.45,0.2727272727272727,0.4445454545454545,0.1268412599833342,71,0.3333333333333333,0.3333333333333333,0.5333333333333333,0.6,0.2,0.39999999999999997,0.1460593486680443,46,0.3703703703703704,0.3703703703703704,0.5925925925925926,0.5142857142857143,0.23076923076923075,0.4156776556776557,0.1259516335201831,51,MLPClassifier,,,,,,,logistic,"(100, 20)",adaptive,2000,lbfgs
+0.36882634162902833,0.010158098420913704,0.0036492347717285156,0.00017554764998244988,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'sgd'}",0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,105,MLPClassifier,,,,,,,logistic,"(100, 20)",adaptive,2000,sgd
+1.0779379844665526,0.23107617716201515,0.0036743640899658202,0.00010460753113904743,"{'activation': 'logistic', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'adam'}",0.5555555555555556,0.6363636363636364,0.6363636363636364,0.5555555555555556,0.25,0.5267676767676768,0.14302473880633854,50,0.3333333333333333,0.4666666666666667,0.4666666666666667,0.6666666666666666,0.13333333333333333,0.4133333333333333,0.17587874611030557,42,0.4166666666666667,0.5384615384615385,0.5384615384615385,0.606060606060606,0.1739130434782609,0.4547126786257222,0.1531207839004028,20,MLPClassifier,,,,,,,logistic,"(100, 20)",adaptive,2000,adam
+0.0735023021697998,0.04297280081143553,0.003200244903564453,0.00010898346659552431,"{'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'lbfgs'}",0.5625,0.3157894736842105,0.45,0.4375,0.21428571428571427,0.39601503759398493,0.11984515762755112,87,0.6,0.4,0.6,0.4666666666666667,0.2,0.4533333333333334,0.1484737163421339,16,0.5806451612903225,0.35294117647058826,0.5142857142857143,0.45161290322580644,0.20689655172413796,0.4212763013993139,0.1308417824463024,43,MLPClassifier,,,,,,,tanh,"(10,)",constant,2000,lbfgs
+0.23330154418945312,0.025409584035756685,0.002823209762573242,4.362906260465709e-05,"{'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'sgd'}",0.625,0.42857142857142855,0.5454545454545454,0.625,0.5,0.5448051948051947,0.07534006784062329,44,0.3333333333333333,0.2,0.4,0.3333333333333333,0.26666666666666666,0.30666666666666664,0.06798692684790379,85,0.43478260869565216,0.27272727272727276,0.4615384615384615,0.43478260869565216,0.3478260869565218,0.3903314077227121,0.07022776930880646,80,MLPClassifier,,,,,,,tanh,"(10,)",constant,2000,sgd
+0.30579495429992676,0.026880923729556885,0.0028073787689208984,4.468246601668189e-05,"{'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'adam'}",0.625,0.75,0.6666666666666666,0.6470588235294118,0.3333333333333333,0.6044117647058823,0.1419843442696606,25,0.3333333333333333,0.4,0.5333333333333333,0.7333333333333333,0.2,0.44000000000000006,0.18184242262647807,24,0.43478260869565216,0.5217391304347827,0.5925925925925926,0.6875,0.25,0.49732286634460543,0.148942741280385,4,MLPClassifier,,,,,,,tanh,"(10,)",constant,2000,adam
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+1.8049395561218262,0.10115705318271331,0.003344440460205078,8.549340523387506e-05,"{'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'adam'}",0.4166666666666667,0.625,0.5294117647058824,0.6153846153846154,0.35714285714285715,0.5087211807800044,0.1065607564812983,53,0.3333333333333333,0.3333333333333333,0.6,0.5333333333333333,0.3333333333333333,0.42666666666666664,0.11623730516108463,31,0.3703703703703704,0.43478260869565216,0.5625,0.5714285714285715,0.3448275862068965,0.4567818273402981,0.09466241660230847,18,MLPClassifier,,,,,,,tanh,"(50,)",adaptive,2000,adam
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+2.048261785507202,0.12304296298465582,0.0035881996154785156,8.7318115225509e-05,"{'activation': 'tanh', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'adam'}",0.42857142857142855,0.6363636363636364,0.5882352941176471,0.4666666666666667,0.4,0.5039674051438757,0.09221166830710863,54,0.4,0.4666666666666667,0.6666666666666666,0.4666666666666667,0.4,0.48,0.0979795897113271,12,0.4137931034482759,0.5384615384615385,0.625,0.4666666666666667,0.4000000000000001,0.48878426171529626,0.08368759303345308,5,MLPClassifier,,,,,,,tanh,"(100, 20)",constant,2000,adam
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+2.061586618423462,0.06256793066074529,0.003636884689331055,7.811320390904681e-05,"{'activation': 'tanh', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'adam'}",0.3333333333333333,0.6,0.5294117647058824,0.46153846153846156,0.38461538461538464,0.4617797888386124,0.09605394982307672,64,0.26666666666666666,0.4,0.6,0.4,0.3333333333333333,0.39999999999999997,0.1115546702045434,46,0.2962962962962963,0.48,0.5625,0.42857142857142855,0.3571428571428571,0.4249021164021164,0.0928581632166312,39,MLPClassifier,,,,,,,tanh,"(100, 20)",adaptive,2000,adam
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+0.26506819725036623,0.03966394456860825,0.0028215885162353516,9.204162755871538e-05,"{'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'sgd'}",0.2857142857142857,1.0,0.4,0.0,0.3333333333333333,0.4038095238095238,0.3278515232772188,83,0.13333333333333333,0.13333333333333333,0.13333333333333333,0.0,0.2,0.12000000000000002,0.06531972647421809,99,0.18181818181818182,0.23529411764705882,0.2,0.0,0.25,0.17342245989304814,0.09004934765138274,94,MLPClassifier,,,,,,,relu,"(10,)",constant,2000,sgd
+0.34603633880615237,0.0649265436773322,0.00281219482421875,8.735279320805418e-05,"{'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'adam'}",0.38461538461538464,0.8,0.6153846153846154,0.5454545454545454,0.5,0.5690909090909091,0.13771388168398224,40,0.3333333333333333,0.26666666666666666,0.5333333333333333,0.4,0.3333333333333333,0.3733333333333333,0.09043106644167025,54,0.3571428571428571,0.4,0.5714285714285715,0.4615384615384615,0.4,0.43802197802197806,0.07454276354002475,31,MLPClassifier,,,,,,,relu,"(10,)",constant,2000,adam
+0.10606822967529297,0.053533481321843664,0.0027156352996826174,2.9628206893612443e-05,"{'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'lbfgs'}",0.3333333333333333,0.375,0.4,0.3125,0.2727272727272727,0.33871212121212124,0.045040029130305526,93,0.4666666666666667,0.4,0.5333333333333333,0.3333333333333333,0.2,0.3866666666666666,0.11469767022723501,50,0.3888888888888889,0.38709677419354843,0.4571428571428572,0.3225806451612903,0.23076923076923075,0.3572956792311631,0.07624928962977434,86,MLPClassifier,,,,,,,relu,"(10,)",invscaling,2000,lbfgs
+0.12356314659118653,0.12421942190111056,0.0026909828186035155,6.570781436380588e-05,"{'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'sgd'}",0.1,0.09243697478991597,0.3333333333333333,0.10067114093959731,0.2727272727272727,0.17983374435802385,0.10243990558139315,97,1.0,0.7333333333333333,0.06666666666666667,1.0,0.2,0.6,0.3955305859784354,4,0.18181818181818182,0.16417910447761194,0.1111111111111111,0.18292682926829268,0.23076923076923075,0.17416089148888564,0.03850945523905075,93,MLPClassifier,,,,,,,relu,"(10,)",invscaling,2000,sgd
+0.3445711612701416,0.0712838935691839,0.0027398586273193358,4.1858155276809265e-05,"{'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'adam'}",0.5,0.75,0.6666666666666666,0.5833333333333334,0.2222222222222222,0.5444444444444445,0.18138697463682787,45,0.3333333333333333,0.2,0.5333333333333333,0.4666666666666667,0.13333333333333333,0.3333333333333333,0.1520233900132184,62,0.4,0.31578947368421056,0.5925925925925926,0.5185185185185186,0.16666666666666669,0.3987134502923977,0.1501107105368813,77,MLPClassifier,,,,,,,relu,"(10,)",invscaling,2000,adam
+0.07240762710571289,0.03113599689325272,0.0027058601379394533,6.269518350269662e-05,"{'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'lbfgs'}",0.3181818181818182,0.6666666666666666,0.46153846153846156,0.38095238095238093,0.29411764705882354,0.4242913948796302,0.13432485500860764,76,0.4666666666666667,0.5333333333333333,0.4,0.5333333333333333,0.3333333333333333,0.4533333333333333,0.077746025264604,20,0.3783783783783784,0.5925925925925926,0.42857142857142855,0.4444444444444444,0.3125,0.4312973687973688,0.09285435902258966,35,MLPClassifier,,,,,,,relu,"(10,)",adaptive,2000,lbfgs
+0.2610966682434082,0.014935323166215772,0.0028059959411621095,5.1771517082965966e-05,"{'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'sgd'}",0.6666666666666666,0.5714285714285714,0.8,0.4444444444444444,0.6666666666666666,0.6298412698412699,0.11785604723984365,17,0.26666666666666666,0.26666666666666666,0.26666666666666666,0.26666666666666666,0.26666666666666666,0.26666666666666666,0.0,93,0.3809523809523809,0.36363636363636365,0.4,0.33333333333333337,0.3809523809523809,0.3717748917748918,0.022401332126470948,83,MLPClassifier,,,,,,,relu,"(10,)",adaptive,2000,sgd
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+0.04955000877380371,0.00372355649319816,0.002831697463989258,2.034983231097393e-05,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'lbfgs'}",0.4375,0.5384615384615384,0.47058823529411764,0.35714285714285715,0.21428571428571427,0.4035956690368455,0.1111957130722278,84,0.4666666666666667,0.4666666666666667,0.5333333333333333,0.3333333333333333,0.2,0.4,0.11925695879998878,44,0.45161290322580644,0.5,0.5,0.3448275862068965,0.20689655172413796,0.40066740823136826,0.1122449122147155,67,MLPClassifier,,,,,,,relu,"(50,)",constant,2000,lbfgs
+0.3405783653259277,0.042148921484528666,0.003117942810058594,3.7836840712640754e-05,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'sgd'}",0.6666666666666666,1.0,0.7142857142857143,0.4444444444444444,0.8333333333333334,0.7317460317460317,0.18404486400807296,3,0.26666666666666666,0.26666666666666666,0.3333333333333333,0.26666666666666666,0.3333333333333333,0.29333333333333333,0.03265986323710903,88,0.3809523809523809,0.4210526315789474,0.4545454545454545,0.33333333333333337,0.47619047619047616,0.41321485532011853,0.05129815357353517,54,MLPClassifier,,,,,,,relu,"(50,)",constant,2000,sgd
+1.406182336807251,0.12741938724800464,0.003235006332397461,0.00012487386517305267,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'adam'}",0.38461538461538464,0.6666666666666666,0.5555555555555556,0.6363636363636364,0.36363636363636365,0.5213675213675214,0.12576568275645825,51,0.3333333333333333,0.5333333333333333,0.3333333333333333,0.4666666666666667,0.26666666666666666,0.3866666666666666,0.09797958971132713,50,0.3571428571428571,0.5925925925925926,0.4166666666666667,0.5384615384615385,0.30769230769230765,0.44251119251119253,0.10757575883244426,27,MLPClassifier,,,,,,,relu,"(50,)",constant,2000,adam
+0.052970504760742186,0.005452908684095196,0.0033062458038330077,2.9562211757353154e-05,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'lbfgs'}",0.35294117647058826,0.4666666666666667,0.5789473684210527,0.3125,0.3,0.4022110423116615,0.10612534517325908,85,0.4,0.4666666666666667,0.7333333333333333,0.3333333333333333,0.2,0.42666666666666664,0.1768866554856213,31,0.37500000000000006,0.4666666666666667,0.6470588235294117,0.3225806451612903,0.24,0.4102612270714737,0.13940932488665292,59,MLPClassifier,,,,,,,relu,"(50,)",invscaling,2000,lbfgs
+0.1470343589782715,0.06399741224635598,0.0034874439239501952,0.0001061789693931547,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'sgd'}",0.11392405063291139,0.12264150943396226,0.1095890410958904,0.0,0.10273972602739725,0.08977886543803226,0.0453511080011298,101,0.6,0.8666666666666667,0.5333333333333333,0.0,1.0,0.6,0.34512477614786097,4,0.19148936170212763,0.21487603305785125,0.18181818181818182,0.0,0.18633540372670807,0.15490379606097376,0.07828497809788673,97,MLPClassifier,,,,,,,relu,"(50,)",invscaling,2000,sgd
+1.6083802223205566,0.301994580639607,0.004079341888427734,0.001223078545507362,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'adam'}",0.46153846153846156,0.45454545454545453,0.6666666666666666,0.42857142857142855,0.2222222222222222,0.4467088467088467,0.14098651209890012,69,0.4,0.3333333333333333,0.6666666666666666,0.4,0.13333333333333333,0.3866666666666666,0.17074997966487596,50,0.42857142857142855,0.3846153846153846,0.6666666666666666,0.4137931034482759,0.16666666666666669,0.41206264999368447,0.15878997484418497,56,MLPClassifier,,,,,,,relu,"(50,)",invscaling,2000,adam
+0.054295587539672854,0.006501607057676768,0.0033596038818359377,4.2841211503933764e-05,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'lbfgs'}",0.38461538461538464,0.5,0.5,0.3888888888888889,0.25,0.4047008547008547,0.09246583477810955,82,0.3333333333333333,0.4,0.4666666666666667,0.4666666666666667,0.2,0.3733333333333334,0.09977753031397177,53,0.3571428571428571,0.4444444444444445,0.4827586206896552,0.42424242424242425,0.22222222222222224,0.3861621137483207,0.09153822213642739,82,MLPClassifier,,,,,,,relu,"(50,)",adaptive,2000,lbfgs
+0.42258143424987793,0.02879488291820619,0.0033105850219726563,6.506592852613603e-05,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'sgd'}",0.5714285714285714,0.5,0.8,0.5,1.0,0.6742857142857143,0.1965830561345094,7,0.26666666666666666,0.2,0.26666666666666666,0.26666666666666666,0.2,0.24,0.03265986323710903,95,0.36363636363636365,0.28571428571428575,0.4,0.3478260869565218,0.33333333333333337,0.34610201392810086,0.03748369158197312,87,MLPClassifier,,,,,,,relu,"(50,)",adaptive,2000,sgd
+1.624642276763916,0.19872194693441425,0.0034543991088867186,0.0003602590534645065,"{'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'adam'}",0.38461538461538464,0.75,0.6428571428571429,0.46153846153846156,0.42857142857142855,0.5335164835164835,0.13941759584310953,48,0.3333333333333333,0.6,0.6,0.4,0.2,0.42666666666666664,0.155492050529208,31,0.3571428571428571,0.6666666666666665,0.6206896551724138,0.42857142857142855,0.27272727272727276,0.46915957605612774,0.15149337182982042,11,MLPClassifier,,,,,,,relu,"(50,)",adaptive,2000,adam
+0.12689056396484374,0.019380635646914092,0.0039041519165039064,0.0005076504639596825,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'lbfgs'}",0.375,0.3888888888888889,0.5555555555555556,0.5,0.2777777777777778,0.41944444444444445,0.09797328938082443,79,0.4,0.4666666666666667,0.6666666666666666,0.4666666666666667,0.3333333333333333,0.4666666666666667,0.11155467020454339,13,0.38709677419354843,0.42424242424242425,0.606060606060606,0.4827586206896552,0.303030303030303,0.4406377456433074,0.10120290639445545,28,MLPClassifier,,,,,,,relu,"(100, 20)",constant,2000,lbfgs
+0.6938549995422363,0.0685513963084081,0.003431224822998047,7.781649329966713e-05,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'sgd'}",0.5714285714285714,1.0,0.75,0.5555555555555556,0.6666666666666666,0.7087301587301587,0.1616322013273373,4,0.26666666666666666,0.2,0.4,0.3333333333333333,0.26666666666666666,0.29333333333333333,0.06798692684790379,88,0.36363636363636365,0.33333333333333337,0.5217391304347827,0.4166666666666667,0.3809523809523809,0.4032655750047055,0.06507602329837384,66,MLPClassifier,,,,,,,relu,"(100, 20)",constant,2000,sgd
+1.1876333236694336,0.08979259701925384,0.0034419536590576173,0.0001254144819289782,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'constant', 'max_iter': 2000, 'solver': 'adam'}",0.35714285714285715,0.7,0.6666666666666666,0.5454545454545454,0.38461538461538464,0.5307758907758908,0.14059152815711978,49,0.3333333333333333,0.4666666666666667,0.6666666666666666,0.4,0.3333333333333333,0.44000000000000006,0.12364824660660938,24,0.3448275862068965,0.56,0.6666666666666666,0.4615384615384615,0.3571428571428571,0.47803511431097645,0.12241848550740289,8,MLPClassifier,,,,,,,relu,"(100, 20)",constant,2000,adam
+0.10192203521728516,0.007974414784472122,0.0034714221954345705,0.00012783357681436726,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'lbfgs'}",0.4666666666666667,0.4,0.5789473684210527,0.4,0.26666666666666666,0.4224561403508772,0.10170839856152558,77,0.4666666666666667,0.4,0.7333333333333333,0.4,0.26666666666666666,0.4533333333333333,0.154344492037203,20,0.4666666666666667,0.4000000000000001,0.6470588235294117,0.4000000000000001,0.26666666666666666,0.4360784313725491,0.12389674608676417,32,MLPClassifier,,,,,,,relu,"(100, 20)",invscaling,2000,lbfgs
+0.09777464866638183,0.027837176788495482,0.003357219696044922,0.0001439564509974002,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'sgd'}",0.22580645161290322,0.0,0.0,0.5714285714285714,1.0,0.35944700460829493,0.3824940313849434,90,0.4666666666666667,0.0,0.0,0.26666666666666666,0.06666666666666667,0.16,0.18184242262647807,98,0.3043478260869565,0.0,0.0,0.36363636363636365,0.125,0.158596837944664,0.15147225433874276,96,MLPClassifier,,,,,,,relu,"(100, 20)",invscaling,2000,sgd
+1.173351764678955,0.03771937510383487,0.0034332275390625,5.926512339824602e-05,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'invscaling', 'max_iter': 2000, 'solver': 'adam'}",0.4,0.7,0.6666666666666666,0.7,0.25,0.5433333333333333,0.1848723283181606,46,0.4,0.4666666666666667,0.6666666666666666,0.4666666666666667,0.13333333333333333,0.42666666666666664,0.17178798302300166,31,0.4000000000000001,0.56,0.6666666666666666,0.56,0.1739130434782609,0.47211594202898555,0.171710732073877,10,MLPClassifier,,,,,,,relu,"(100, 20)",invscaling,2000,adam
+0.15829339027404785,0.07911208262281175,0.0034626007080078127,0.00010584059995727836,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'lbfgs'}",0.4166666666666667,0.5454545454545454,0.5,0.5625,0.3333333333333333,0.47159090909090906,0.08562617087467662,60,0.3333333333333333,0.4,0.6,0.6,0.26666666666666666,0.44000000000000006,0.13727506854649332,24,0.3703703703703704,0.4615384615384615,0.5454545454545454,0.5806451612903225,0.2962962962962963,0.4508609669899992,0.10608945719305136,21,MLPClassifier,,,,,,,relu,"(100, 20)",adaptive,2000,lbfgs
+0.7347156524658203,0.07182041177507488,0.0034441471099853514,6.154994385330038e-05,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'sgd'}",0.625,1.0,0.6666666666666666,0.4,0.8,0.6983333333333333,0.19835434062415785,5,0.3333333333333333,0.26666666666666666,0.26666666666666666,0.13333333333333333,0.26666666666666666,0.2533333333333333,0.06531972647421809,94,0.43478260869565216,0.4210526315789474,0.3809523809523809,0.2,0.4,0.367357524245396,0.08566002432699567,85,MLPClassifier,,,,,,,relu,"(100, 20)",adaptive,2000,sgd
+1.2371012687683105,0.07587795277792217,0.003482151031494141,7.824190178678591e-05,"{'activation': 'relu', 'hidden_layer_sizes': (100, 20), 'learning_rate': 'adaptive', 'max_iter': 2000, 'solver': 'adam'}",0.4666666666666667,0.5833333333333334,0.5625,0.5833333333333334,0.23076923076923078,0.4853205128205128,0.1343979318154324,58,0.4666666666666667,0.4666666666666667,0.6,0.4666666666666667,0.2,0.44000000000000006,0.13063945294843615,24,0.4666666666666667,0.5185185185185186,0.5806451612903225,0.5185185185185186,0.21428571428571427,0.45972691585594816,0.12792472062564747,15,MLPClassifier,,,,,,,relu,"(100, 20)",adaptive,2000,adam

bug-2022/requirements.txt

@@ -1,2 +1,3 @@
 javalang==0.13.0
 pandas==1.5.2
+scikit_learn==1.2.1

bug-2022/train_classifiers.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+import os
+import pandas as pd
+import glob
+
+from sklearn import preprocessing
+from sklearn.metrics import f1_score, precision_score, recall_score, make_scorer
+from sklearn.model_selection import GridSearchCV
+
+from sklearn.neural_network import MLPClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+
+DIR: str = os.path.dirname(os.path.realpath(__file__))
+IN_DIR: str = DIR + '/metrics'
+OUT_DIR: str = DIR + '/models'
+RANDOM: int = 42
+
+
+def clean_output():
+    filelist = glob.glob(OUT_DIR + '/models.csv')
+    for f in filelist:
+        os.remove(f)
+
+
+def get_classifiers() -> list:
+    return [
+        (GaussianNB(), {}),
+        (SVC(), {
+            'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
+            'gamma': ['scale', 'auto']
+        }),
+        (DecisionTreeClassifier(), {
+            'criterion': ['gini', 'entropy'],
+            'splitter': ['best', 'random']
+        }),
+        (RandomForestClassifier(), {
+            'criterion': ['gini', 'entropy'],
+            'max_features': ['sqrt', 'log2'],
+            'class_weight': ['balanced', 'balanced_subsample']
+        }),
+        (MLPClassifier(), {
+            'max_iter': [2000],
+            'hidden_layer_sizes': [(10,), (50,), (100, 20,)],
+            'activation':  ['identity', 'logistic', 'tanh', 'relu'],
+            'solver': ['lbfgs', 'sgd', 'adam'],
+            'learning_rate': ['constant', 'invscaling', 'adaptive']
+        })
+    ]
+
+
+def main():
+    clean_output()
+
+    # load dataset
+    df = pd.read_csv(IN_DIR + '/feature_vectors_labeled.csv')
+    df = df.drop('class_name', axis=1)
+
+    X = df.iloc[:, :-1].to_numpy()
+    y = df.iloc[:, -1].to_numpy()
+
+    scaler = preprocessing.StandardScaler().fit(X)
+    X_scaled = scaler.transform(X)
+
+    df = None
+
+    for classifier, grid in get_classifiers():
+        # cross-validation splits are same across calls as data is not shuffled
+        # see: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
+        clf = GridSearchCV(classifier, grid, cv=5, scoring={
+            'precision': make_scorer(precision_score, average='binary', zero_division=0),
+            'recall': make_scorer(recall_score, average='binary'),
+            'f1': make_scorer(f1_score, average='binary')
+        }, refit='f1')
+
+        clf.fit(X_scaled, y)
+
+        df_classifier = pd.DataFrame(clf.cv_results_)
+        df_classifier['classifier'] = type(classifier).__name__
+
+        df = df_classifier if df is None else pd.concat(
+            [df, df_classifier], ignore_index=True)
+
+    df.to_csv(OUT_DIR + '/models.csv', index=False)
+
+
+if __name__ == '__main__':
+    main()