In [1]:
# Import libraries
import pandas as pd
import glob
import pickle
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn import svm
from sklearn.neural_network import MLPClassifier
import warnings
warnings.filterwarnings('ignore')
In [75]:
category_list = ["IT4060", "IT4130", "SE4010", "SE1234", "Notice"]
directory_list = ["Dataset/IT4060/*.txt", "Dataset/IT4130/*.txt", "Dataset/SE4010/*.txt", "Dataset/SE1234/*.txt", "Dataset/Notices/*.txt"]
text_files = list(map(lambda x: glob.glob(x), directory_list))
text_files = [item for sublist in text_files for item in sublist]
dataset = []
data = ""
for file in text_files:
f = open(file, 'r', encoding='utf-8')
f = f.read()
file = f.split('\n')
last_line = file[len(file)-1]
file = file[:-1]
data = ""
for i in range(len(file)):
data += file[i]
dataset.append({'data' : data, 'flag' : last_line})
dataset[200]
Out[75]:
{'data': 'VERY IMPORTANT - FINAL THESIS PROOFREAD VERIONYou are required to submit your final corrected thesis and proofreader documents to the cloud folder on or before 26th February 2021. Please follow the instructions given below.',
'flag': 'Notice'}
In [76]:
dataset = pd.DataFrame(dataset, columns=['data','flag'])
dataset.to_csv("dataset.csv", sep=',', encoding='utf-8')
print(dataset.shape)
(205, 2)
In [77]:
# Get Vector Count
vec_count = CountVectorizer()
X_train_count = vec_count.fit_transform(dataset.data)
print(vec_count.vocabulary_)
# Save word vector
pickle.dump(vec_count.vocabulary_, open("vec_count.pkl", "wb"))
{'featuresthe': 830, 'number': 1404, 'of': 1414, 'features': 829, 'or': 1442, 'distinct': 682, 'traits': 2117, 'that': 2054, 'can': 331, 'be': 236, 'used': 2169, 'to': 2092, 'describe': 630, 'each': 718, 'item': 1120, 'in': 1029, 'quantitative': 1645, 'manner': 1251, 'samplesa': 1789, 'sample': 1787, 'is': 1112, 'an': 99, 'process': 1591, 'classify': 398, 'it': 1118, 'document': 693, 'picture': 1525, 'sound': 1929, 'video': 2207, 'row': 1774, 'database': 573, 'csv': 560, 'file': 837, 'whatever': 2252, 'you': 2308, 'with': 2269, 'fixed': 867, 'set': 1862, 'feature': 828, 'vector': 2195, 'dimensional': 669, 'numerical': 1406, 'represent': 1727, 'some': 1925, 'object': 1407, 'supermarket': 2003, 'may': 1275, 'store': 1969, 'buyer': 316, 'basket': 231, 'content': 497, 'details': 639, 'there': 2068, 'are': 158, 'no': 1383, 'grouping': 945, 'labels': 1156, 'need': 1365, 'group': 944, 'the': 2056, 'buyers': 317, 'based': 227, 'on': 1423, 'their': 2062, 'buying': 318, 'patterns': 1499, 'order': 1444, 'best': 253, 'use': 2168, 'shelf': 1877, 'space': 1932, 'recommendation': 1677, 'when': 2253, 'data': 572, 'availablea': 210, 'reward': 1765, 'function': 897, 'measure': 1283, 'for': 878, 'given': 925, 'actionbased': 60, 'values': 2186, 'probability': 1584, 'distribution': 688, 'obtained': 1412, 'functions': 900, 'since': 1892, 'cost': 533, 'not': 1395, 'convex': 511, 'now': 1402, 'we': 2237, 'might': 1309, 'get': 920, 'stuck': 1979, 'local': 1212, 'minimano': 1312, 'straight': 1972, 'forward': 882, 'solution': 1922, 'thishave': 2080, 'try': 2132, 'different': 660, 'initial': 1055, 'θlearning': 2318, 'ratefeature': 1662, 'scalingoverfitting': 1798, 'and': 108, 'underfitting': 2148, 'bias': 259, 'variance': 2191, 'linear': 1199, 'classifier': 397, 'relies': 1713, 'dot': 706, 'product': 1601, 'between': 255, 'vectors': 2196, 'xi': 2296, 'xj': 2297, 'xjif': 2298, 'every': 790, 'point': 1541, 'mapped': 1254, 'into': 1099, 'high': 973, 'via': 2205, 'transformation': 2120, 'becomes': 238, 'kernel': 1143, 'corresponds': 532, 'inner': 1063, 'expanded': 802, 'attribute': 195, 'listthe': 1204, 'candidate': 333, 'attributesattribute_selection_methoda': 197, 'procedure': 1588, 'determine': 645, 'splitting': 1943, 'criterion': 548, 'partitions': 1486, 'samples': 1788, 'individual': 1043, 'classes': 392, 'this': 2079, 'consists': 485, 'splitting_attribure': 1944, 'possibly': 1555, 'either': 732, 'split': 1942, 'subset': 1993, 'output': 1464, 'decision': 592, 'tree': 2127, 'unsupervised': 2159, 'learning': 1170, 'applied': 142, 'unlabeled': 2156, 'datatwo': 576, 'main': 1235, 'applications': 137, 'clustering': 412, 'mining': 1314, 'systems': 2018, 'dimensionality': 670, 'reduction': 1685, 'compression': 449, 'visualization': 2214, 'entropy': 778, 'partition': 1485, 'measures': 1286, 'level': 1186, 'impurity': 1028, 'expected': 804, 'information': 1050, 'needed': 1366, 'minimum': 1313, 'by': 319, 'info': 1049, 'input': 1065, 'training': 2116, 'associated': 179, 'class': 391, 'continued': 499, 'over': 1467, 'time': 2088, 'also': 94, 'deployed': 624, 'both': 285, 'single': 1893, 'multi': 1351, 'agent': 79, 'systemsalgorithms': 2019, 'actor': 63, 'critic': 549, 'monte': 1336, 'carlo': 347, 'methods': 1301, 'etc': 787, 'web': 2238, 'page': 1472, 'classificationspeech': 395, 'text': 2050, 'conversionvideo': 507, 'image': 1008, 'generation': 916, 'extractionpreparation': 816, 'transforms': 2122, 'fewer': 835, 'dimensions': 671, 'evolution': 792, 'discover': 676, 'potentially': 1557, 'predictive': 1563, 'relationships': 1707, 'difference': 658, 'anomaly': 121, 'detection': 642, 'supervised': 2004, 'learninganomaly': 1171, 'fraud': 891, 'instead': 1077, 'deals': 587, 'problem': 1585, 'finding': 859, 'depart': 621, 'significantly': 1886, 'from': 895, 'behaviorensemble': 244, 'adaboost': 64, 'algorithm': 82, 'treesadaboost': 2129, 'implementation': 1022, 'boosting': 283, 'technique': 2037, 'as': 169, 'ensemble': 774, 'method': 1300, 'ml': 1319, 'its': 1124, 'weights': 2244, 're': 1665, 'assigned': 175, 'instance': 1073, 'higher': 974, 'incorrectly': 1034, 'classified': 396, 'instances': 1074, 'using': 2180, 'boost': 282, 'performance': 1512, 'trees': 2128, 'binary': 261, 'classification': 394, 'problems': 1586, 'hidden': 972, 'markov': 1260, 'solid': 1921, 'statistical': 1958, 'foundation': 886, 'efficient': 731, 'flexible': 870, 'generative': 917, 'model': 1326, 'sequence': 1851, 'properties': 1620, 'if': 1004, 'have': 963, 'so': 1915, 'many': 1252, 'states': 1956, 'system': 2017, 'predict': 1562, 'future': 903, 'state': 1955, 'only': 1428, 'current': 562, 'speech': 1939, 'recognition': 1674, 'malware': 1244, 'pattern': 1497, 'naive': 1357, 'bayesian': 233, 'fast': 826, 'train': 2114, 'sensitive': 1841, 'irrelevant': 1110, 'handle': 954, 'real': 1669, 'discrete': 677, 'streaming': 1974, 'machine': 1232, 'big': 260, 'gpgpu': 937, 'computinguse': 466, 'large': 1162, 'unstructured': 2157, 'sets': 1864, 'call': 325, 'records': 1678, 'social': 1917, 'media': 1288, 'two': 2138, 'approachesuse': 147, 'platform': 1534, 'apache': 127, 'hadoop': 951, 'spark': 1933, 'advantage': 73, 'density': 620, 'means': 1281, 'define': 602, 'complex': 444, 'cluster': 411, 'shapes': 1871, 'automatically': 205, 'find': 858, 'outliersadvantages': 1461, 'dbscan': 582, 'algorithmsparameters': 86, 'specifies': 1937, 'how': 984, 'close': 405, 'points': 1542, 'should': 1880, 'other': 1456, 'considered': 484, 'part': 1481, 'form': 880, 'dense': 619, 'region': 1701, 'resistant': 1749, 'noise': 1385, 'clusters': 413, 'sizesfeature': 1898, 'scaling': 1797, 'make': 1239, 'sure': 2008, 'all': 90, 'similar': 1888, 'scale': 1795, 'value': 2185, 'range': 1657, 'starts': 1952, 'dominating': 702, 'calculating': 324, 'distances': 681, 'cloud': 407, 'analytics': 106, 'amazon': 98, 'aws': 216, 'services': 1858, 'microsoft': 1304, 'azure': 218, 'gpus': 938, 'speed': 1940, 'up': 2162, 'particularly': 1484, 'deep': 599, 'gradient': 940, 'descent': 629, 'polynomial': 1546, 'regression': 1702, 'market': 1259, 'segmentationsocial': 1826, 'network': 1377, 'analysisastronomical': 102, 'analysisorganize': 104, 'computing': 464, 'algorithmsk': 85, 'meansself': 1282, 'organizing': 1449, 'mapsauto': 1255, 'encoders': 748, 'belief': 247, 'networks': 1379, 'pcacompression': 1503, 'reduce': 1682, 'memory': 1293, 'pca': 1502, 'approachmore': 148, 'run': 1778, 'entire': 776, 'multiple': 1352, 'times': 2089, 'matrix': 1269, 'singular': 1894, 'decomposition': 593, 'diagonal': 656, 'why': 2261, 'kernels': 1144, 'non': 1390, 'separable': 1847, 'map': 1253, 'better': 254, 'representational': 1729, 'svm': 2012, 'hypertext': 995, 'categorization': 351, 'support': 2005, 'regressionfit': 1703, 'trying': 2133, 'maximize': 1273, 'margin': 1257, 'positive': 1552, 'negative': 1368, 'separating': 1849, 'hyperplane': 994, 'most': 1341, 'important': 1024, 'they': 2076, 'quadratic': 1643, 'optimization': 1441, 'algorithms': 84, 'identify': 1001, 'which': 2256, 'zero': 2313, 'lagrangian': 1159, 'multipliers': 1354, 'αi': 2317, 'dual': 713, 'formulation': 881, 'appear': 131, 'inside': 1070, 'products': 1603, 'new': 1380, 'score': 1806, 'projection': 1613, 'onto': 1430, 'normal': 1394, 'compute': 459, 'σαiyixi': 2319, 'bdecide': 235, 'whether': 2255, '1can': 19, 'confidence': 472, 'threshold': 2084, 'svmnon': 2013, 'parameterized': 1479, 'vs': 2221, 'overfitting': 1468, 'controlled': 505, 'soft': 1918, 'svmsthe': 2014, 'original': 1454, 'always': 97, 'where': 2254, 'reword': 1767, 'scaler': 1796, 'feedback': 832, 'signal': 1883, 'indicate': 1040, 'well': 2246, 'doing': 698, 'step': 1964, 'actions': 61, 'cumulative': 561, 'hypothesis': 996, 'goals': 930, 'described': 631, 'maximization': 1272, 'rewards': 1766, 'bfgs': 256, 'conjugate': 477, 'probabilistic': 1583, 'prior': 1577, 'knowledge': 1151, 'about': 49, 'viterbi': 2215, 'algorithmbaum': 83, 'welch': 2245, 'learningrobot': 1174, 'localizationpart': 1213, 'tagginggeneticspeech': 2023, 'recognitions': 1676, 'measured': 1284, 'bits': 264, 'purity': 1639, 'calculate': 322, 'gain': 905, 'array': 166, 'restrict': 1757, 'children': 383, 'per': 1509, 'node': 1384, 'ary': 168, 'has': 960, 'branchesternary': 291, 'branchesn': 290, 'branches': 289, 'inductiona': 1044, 'top': 2105, 'down': 707, 'approach': 145, 'common': 434, 'start': 1951, 'recursively': 1680, 'smaller': 1912, 'subsets': 1995, 'being': 246, 'built': 311, 'done': 703, 'test': 2048, 'rule': 1777, 'variable': 2189, 'dataset': 575, 'calculated': 323, 'post': 1556, 'pruning': 1631, 'techniques': 2038, 'pruningreduced': 1634, 'error': 782, 'pruningmaximum': 1633, 'pruningerror': 1632, 'complexity': 445, 'learningreinforcement': 1173, 'learningsemi': 1175, 'learningregression': 1172, 'continuous': 500, 'variables': 2190, 'salary': 1782, 'rent': 1719, 'facial': 819, 'tumor': 2134, 'benign': 252, 'malignant': 1243, 'type': 2139, 'vehicle': 2197, 'stage': 1949, 'progression': 1611, 'cancer': 332, 'too': 2101, 'attributes': 196, 'choose': 385, '3d': 41, '2d': 37, 'imagedimensionality': 1010, 'reductionprincipal': 1686, 'component': 446, 'analysis': 101, 'commonly': 435, 'suffering': 1998, 'getting': 923, 'more': 1339, 'help': 969, 'changed': 369, 'increased': 1035, 'made': 1233, 'case': 348, 'else': 738, 'adjust': 69, 'regularization': 1705, 'term': 2044, 'fix': 866, 'been': 239, 'successfully': 1996, 'world': 2283, 'bioinformatics': 262, 'protein': 1624, 'hand': 953, 'written': 2289, 'character': 372, 'recognitioncould': 1675, 'work': 2278, 'even': 789, 'your': 2310, 'relatively': 1708, 'small': 1911, 'processing': 1592, 'improvement': 1026, 'pictorial': 1524, 'human': 991, 'perception': 1510, 'autonomous': 208, 'application': 136, 'storage': 1967, 'transmission': 2124, 'employ': 742, 'capable': 339, 'enhancing': 767, 'interpretation': 1098, 'analysistypical': 105, 'filteringcontent': 846, 'enhancementcontrast': 765, 'enhancement': 764, 'de': 584, 'blurring': 273, 'remote': 1717, 'sensing': 1840, 'what': 2251, 'digitization': 666, 'representation': 1728, 'finite': 862, 'samplingeach': 1791, 'element': 734, 'represented': 1732, 'one': 1425, 'quantization': 1646, 'sampling': 1790, 'display': 678, 'computer': 461, 'images': 1015, 'require': 1741, 'available': 209, 'digital': 664, 'includes': 1031, 'quantizationto': 1647, 'first': 864, 'converted': 509, 'analog': 100, 'scanned': 1799, 'definitiona': 604, 'derived': 627, 'through': 2085, 'frequently': 894, 'referred': 1696, 'spatial': 1934, 'domaindirectly': 700, 'intensity': 1085, 'plane': 1532, 'frequency': 893, 'domain': 699, 'transform': 2119, 'coefficients': 417, 'does': 695, 'directly': 674, 'fall': 823, 'under': 2147, 'broad': 298, 'categories': 350, 'itself': 1125, 'direct': 673, 'manipulation': 1250, 'pixels': 1527, 'imagefrequency': 1011, 'modify': 1330, 'fourier': 887, 'take': 2024, 'inverse': 1103, 'modified': 1329, 'obtain': 1411, 'enhanced': 763, 'iprocessing': 1108, 'enhance': 762, 'certain': 361, 'imagethe': 1017, 'result': 1758, 'suitable': 2000, 'than': 2051, 'specific': 1936, 'applicationsprocessing': 140, 'very': 2204, 'much': 1350, 'orientedbest': 1452, 'ray': 1664, 'microscopic': 1302, 'expanding': 803, 'dynamic': 717, 'achieved': 56, 'contrast': 502, 'low': 1229, 'poor': 1547, 'illumination': 1007, 'imaging': 1019, 'sensors': 1844, 'wrong': 2290, 'settings': 1865, 'acquisition': 57, 'devices': 653, 'capture': 342, 'printing': 1576, 'respond': 1752, 'according': 53, 'power': 1558, 'lawthe': 1165, 'correcting': 527, 'law': 1164, 'response': 1753, 'gamma': 907, 'correctionexample': 529, 'crt': 551, 'voltage': 2219, 'exponents': 811, 'typically': 2141, 'correction': 528, 'could': 538, 'applying': 144, 'highlight': 977, 'intensities': 1084, 'interesttwo': 1095, 'approachesset': 146, 'pixel': 1526, 'within': 2272, 'interest': 1093, 'white': 2258, 'others': 1457, 'another': 122, 'black': 268, 'produces': 1600, 'imagebrighten': 1009, 'darken': 568, 'leave': 1176, 'unchanged': 2145, 'enhancementsimage': 766, 'statisticsimage': 1960, 'compressionimage': 452, 'segmentationgives': 1825, 'useful': 2170, 'analyzing': 107, 'simple': 1889, 'softwareeconomic': 1920, 'hardware': 959, 'implementationspopular': 1023, 'tool': 2102, 'vision': 2211, 'applicationshere': 138, 'procedures': 1589, 'extraction': 815, 'processingtypical': 1597, 'industrial': 1045, 'assembly': 172, 'inspectionautomated': 1071, 'target': 2029, 'trackingfinger': 2113, 'print': 1575, 'composed': 448, 'bitsfor': 266, 'example': 796, '256': 34, 'gray': 942, 'comprised': 455, 'bitswe': 267, 'contribution': 503, 'total': 2107, 'appearance': 132, 'shows': 1881, 'bit': 263, 'slicing': 1902, 'indetermining': 1038, 'necessary': 1364, 'quantize': 1648, 'imageimage': 1012, 'compressionfeature': 451, 'arithmetic': 163, 'logic': 1218, 'operations': 1436, 'extensively': 813, 'applicationsmay': 139, 'cover': 544, 'subsetarithmetic': 1994, 'operation': 1435, 'defined': 603, 'addition': 66, 'often': 1419, 'averaging': 213, 'noisesubtraction': 1386, 'static': 1957, 'background': 221, 'removalmultiplication': 1718, 'pq': 1560, 'correct': 525, 'shadingdivision': 1869, 'multiplication': 1353, 'pandq': 1475, 'porq': 1550, 'complement': 442, 'notq': 1400, 'functionally': 899, 'complete': 443, 'setapplicable': 1863, 'imagesbasic': 1016, 'tools': 2104, 'maskingfeature': 1264, 'detectionshape': 643, 'negativeslogarithmic': 1369, 'compressionpower': 454, 'transformationcontrast': 2121, 'stretchinggray': 1975, 'slicinghistogram': 1903, 'histogram': 980, 'equalization': 781, 'modificationimage': 1327, 'differencingimage': 659, 'mask': 1262, 'smoothing': 1913, 'filter': 840, 'median': 1289, 'nonlinear': 1392, 'sharpening': 1874, 'logical': 1219, 'place': 1528, 'imagetypically': 1018, 'neighborhood': 1371, 'orientedformulated': 1453, 'context': 498, 'called': 326, 'template': 2040, 'window': 2266, 'basic': 229, 'concept': 467, 'let': 1183, 'neighbors': 1375, 'sense': 1839, 'sub': 1989, 'windowsub': 2267, 'near': 1363, 'boundary': 287, 'computed': 460, 'partial': 1482, 'neighborhoods': 1372, 'padding': 1471, 'appropriately': 151, 'correlationin': 531, 'correlation': 530, 'weighted': 2243, 'sum': 2002, 'neighboring': 1374, 'convolutiona': 514, 'convolution': 513, 'rotated': 1772, '180': 14, 'degrees': 607, 'unit': 2154, 'impulse': 1027, 'yields': 2307, 'version': 2203, 'at': 185, 'locationuse': 1215, 'masks': 1265, 'filtering': 845, 'filteringmay': 847, 'nonlinearlinear': 1393, 'filters': 850, 'processingthe': 1596, 'major': 1238, 'emphasis': 741, 'moving': 1347, 'partsthis': 1489, 'various': 2193, 'tracking': 2112, 'targets': 2031, 'security': 1820, 'surveillance': 2010, 'purposeto': 1641, 'out': 1460, 'trajectory': 2118, 'targetmonitoring': 2030, 'movements': 1346, 'organ': 1447, 'boundariesin': 286, 'medical': 1290, 'filterslowpass': 853, 'attenuate': 194, 'eliminate': 737, 'components': 447, 'such': 1997, 'characterized': 376, 'edges': 726, 'sharp': 1873, 'imagenet': 1014, 'effect': 729, 'blurringhighpass': 274, 'slowly': 1910, 'varying': 2194, 'characteristicsnet': 375, 'detailsbandpass': 640, 'rangeused': 1658, 'primarily': 1572, 'restoration': 1756, 'little': 1207, 'operate': 1433, 'neighborhoodstheir': 1373, 'considerationthey': 483, 'do': 692, 'explicitly': 808, 'coefficient': 416, 'filtersexample': 851, 'filtersmedian': 854, 'computes': 463, 'max': 1271, 'brightest': 294, 'imagemin': 1013, 'dimmest': 672, 'will': 2265, 'proper': 1619, 'selection': 1828, 'operationsblurringde': 1437, 'noisingedge': 1387, 'shape': 1870, 'implement': 1021, 'lowpass': 1231, 'indicates': 1041, 'coefficientsfor': 418, '3x3': 42, 'simplest': 1890, 'arrangement': 165, 'equal': 780, 'dividing': 691, 'reductionrandom': 1687, 'results': 1759, 'transitions': 2123, 'levels': 1189, 'blur': 272, 'edgesreducing': 727, 'detailssmaller': 641, 'maskbridging': 1263, 'gaps': 908, 'lines': 1200, 'curves': 564, 'filtersfor': 852, 'statistics': 1959, 'whose': 2260, 'ordering': 1445, 'ranking': 1659, 'contained': 494, 'area': 159, 'encompassed': 749, 'filtercomputes': 841, 'filterused': 855, 'smoothingaveragingone': 1914, 'blurs': 275, 'intent': 1086, 'achieve': 55, 'filteringthe': 848, 'replaced': 1722, 'opposed': 1439, 'average': 212, 'effective': 730, 'strong': 1977, 'spike': 1941, 'like': 1197, 'salt': 1783, 'pepper': 1508, 'edge': 725, 'sharpness': 1875, 'preservedthe': 1567, 'half': 952, 'greater': 943, 'less': 1181, 'mto': 1349, 'sort': 1928, 'assign': 174, 'interestforces': 1094, 'midpoint': 1307, 'simply': 1891, 'maximum': 1274, 'works': 2279, 'randomly': 1656, 'distributed': 684, 'gaussian': 909, 'uniform': 2151, 'alpha': 93, 'mean': 1279, 'suppose': 2006, 'delete': 608, 'lowest': 1230, 'highest': 976, 'axy': 217, 'rk': 1770, 'remaining': 1715, 'mn': 1320, 'any': 124, 'sensor': 1842, 'affected': 76, 'degree': 606, 'thermal': 2071, 'electrical': 733, 'otherwise': 1458, 'these': 2072, 'filtergeometric': 843, 'filterharmonic': 844, 'filtercontraharmonic': 842, 'filterlet': 849, 'coordinates': 520, 'rectangular': 1679, 'subimage': 1990, 'size': 1897, 'mxn': 1356, 'centered': 357, 'compressionan': 450, 'usually': 2182, 'contains': 496, 'lot': 1228, 'redundancy': 1688, 'exploited': 810, 'compressionpixel': 453, 'redundancycoding': 1690, 'redundancypsycho': 1691, 'visual': 2213, 'redundancyapplication': 1689, 'reduced': 1683, 'storagereduced': 1968, 'bandwidth': 224, 'darkest': 569, 'reduces': 1684, 'min': 1310, 'light': 1196, 'brightnessit': 295, 'column': 427, 'indices': 1042, 'specify': 1938, 'identifies': 1000, 'pointthese': 1544, 'elements': 735, 'steps': 1965, 'processingimage': 1594, 'capabilities': 338, 'digitize': 668, 'produced': 1599, 'sensorpreprocessing': 1843, 'quality': 1644, 'segmentation': 1824, 'constitute': 486, 'parts': 1487, 'objects': 1408, 'processingdescription': 1593, 'extracts': 817, 'description': 632, 'further': 902, 'processingrecognition': 1595, 'assigning': 176, 'label': 1154, 'provided': 1626, 'descriptor': 633, 'assigns': 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'requires': 1746, 'building': 308, 'deploying': 625, 'versio': 2202, 'microservices': 1303, 'practice': 1561, 'thebest': 2057, 'job': 1131, 'shared': 1872, 'outside': 1465, 'organization': 1448, 'focus': 874, 'ipc': 1107, 'infrastructure': 1053, 'automation': 206, 'netflix': 1376, 'opensource': 1432, 'favor': 827, 'independent': 1036, 'consumer': 489, 'driven': 710, 'service': 1857, 'contracts': 501, 'tolerant': 2099, 'reader': 1667, 'minimal': 1311, 'heads': 967, 'create': 545, 'opportunity': 1438, 'haveteams': 964, 'responsible': 1754, 'aspects': 171, 'build': 307, '24': 32, 'slas': 1900, 'rather': 1663, 'avoid': 214, 'tight': 2087, 'coupling': 542, 'explicitremote': 809, 'mechanisms': 1287, 'independently': 1037, 'deployable': 623, 'andscalable': 116, 'going': 932, 'deploy': 622, 'host': 982, 'ar': 155, 'begin': 242, 'secure': 1818, 'access': 52, 'camera': 329, 'our': 1459, 'therefore': 2069, 'open': 1431, 'terminal': 2045, 'install': 1072, 'bellow': 248, 'npm': 1403, 'server2': 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'œauthorizedâ': 2314, 'users': 2175, 'kerckhoffâ': 1142, '19th': 18, 'century': 358, 'cryptosystem': 556, 'ifeverything': 1005, 'except': 799, 'ispublic': 1114, 'opposite': 1440, 'securitythrough': 1821, 'obscurityâ': 1410, 'counter': 540, 'enigma': 768, 'wwii': 2292, 'en': 743, 'wiki': 2263, 'enigma_machine': 769, 'historic': 981, 'examplesâ': 797, 'ceaser': 356, 'cipher': 386, 'characteristics': 374, 'good': 933, 'cipherâ': 389, 'confusion': 476, 'interceptor': 1092, 'able': 48, 'topredict': 2106, 'changing': 371, 'inplaintext': 1064, 'ciphertextâ': 388, 'diffusion': 663, 'characteristic': 373, 'distributingthe': 687, 'ciphertext': 387, 'stream': 1973, 'pros': 1622, 'propagationâ': 1618, 'cons': 480, 'susceptible': 2011, 'maliciousinserts': 1242, 'modifications': 1328, 'block': 270, 'immunity': 1020, 'insertionâ': 1069, 'propagation': 1617, 'slow': 1908, 'securityâ': 1822, 'unconditional': 2146, 'canâ': 337, 'bebroken': 237, 'irrespective': 1111, 'computationalpower': 458, 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1856, 'encryptionsymmetric': 754, 'desthe': 637, 'des': 628, 'outline': 1462, 'round': 1773, 'symmetric': 2015, 'encryptionâ': 755, 'issue': 1115, 'confidentiality': 473, 'repudiation': 1733, 'authentication': 200, 'asymmetric': 183, 'gives': 926, 'withencryption': 2271, 'iscomputationally': 1113, 'hard': 958, 'transmissionmechanism': 2125, 'recipient': 1673, 'generates': 915, 'pair': 1473, 'published': 1637, 'trusted': 2131, 'sender': 1833, 'gets': 921, 'uses': 2177, 'encodemessage': 746, 'decrypts': 597, 'privatekey': 1580, 'replies': 1725, 'encoded': 745, 'senderâ': 1835, 'publickey': 1636, 'captured': 343, 'rsa': 1776, 'algorithmâ': 87, 'alice': 89, 'finds': 860, 'prime': 1573, 'qâ': 1653, 'picks': 1523, 'random': 1655, 'andf': 111, 'fâ': 904, 'mod': 1324, 'sends': 1837, 'bobâ': 278, 'encrypts': 756, 'his': 979, 'asym': 182, 'authenticationâ': 201, 'confidentialityâ': 474, 'rid': 1769, 'mostly': 1342, 'pretty': 1568, 'privacyâ': 1578, 'pgpâ': 1520, 'forâ': 884, 'messagesâ': 1297, 'signatures': 1884, 'pgp': 1519, 'combines': 430, 'problemâ': 1587, 'slower': 1909, 'distributionproblemâ': 689, 'encrypt': 750, 'distributekey': 686, 'hash': 961, 'functionsâ': 901, 'condenses': 469, 'unique': 2153, 'valueâ': 2187, 'md': 1277, 'sha': 1867, '512': 44, 'salted': 1784, 'hashing': 962, 'collisionâ': 425, 'googleblog': 936, '2017': 25, '02': 3, 'announcing': 120, 'sha1': 1868, 'collision': 424, 'htmlâ': 987, 'shattered': 1876, 'io': 1104, 'reverse': 1763, 'lookup': 1227, 'domaintools': 701, 'applicationsâ': 141, 'passwordsâ': 1493, 'integrity': 1081, 'protection': 1623, 'digitalsignatures': 665, 'blockchain': 271, 'quantum': 1649, 'computers': 462, 'break': 292, 'likersaâ': 1198, 'tries': 2130, 'come': 431, 'withcryptographic': 2270, 'quantummechanics': 1650, 'referencestransport': 1694, 'layer': 1166, 'tls': 2090, 'transport': 2126, 'vulnerabilities': 2223, 'threats': 2083, 'boolean': 281, 'sql': 1946, 'injectionunion': 1061, 'injectionerror': 1058, 'injectionwasp': 1062, 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'cookies': 516, 'arenot': 161, 'exposedowasp': 812, 'java': 1129, 'encoder': 747, 'appropriateencode': 150, 'locationno': 1214, 'click': 399, 'link': 1201, 'canuse': 336, 'fields': 836, 'wellworks': 2248, 'usehidden': 2171, 'csrfprotection': 559, 'canprevent': 335, 'xss': 2300, 'wellreflective': 2247, 'xssstored': 2301, 'xssxss': 2302, 'vulnerability': 2226, 'scanning': 1800, 'detectpages': 644, 'vulnerabilitye': 2228, 'zed': 2312, 'proxy': 1630, 'doesnâ': 697, 'workthat': 2282, 'stored': 1970, 'false': 824, 'positives': 1553, 'negativessql': 1370, 'injectionsql': 1059, 'injection': 1057, 'demo': 612, 'login': 1220, 'drop': 712, 'tables': 2021, 'databaseshould': 574, 'give': 924, 'priviledges': 1581, 'ddl': 583, 'vsdml': 2222, 'union': 2152, 'democolumn': 613, 'count': 539, 'matchtypes': 1267, 'injectiontime': 1060, '10owasp': 6, '10': 5, '2013': 23, '2017csrf': 26, 'thereframeworks': 2070, 'play': 1536, 'partsecurity': 1488, 'misconfigurationsecurity': 1317, 'misconfiguration': 1316, 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In [78]:
# Transform word vector to TF IDF
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_count)
print(X_train_tfidf)
# Save TF IDF
pickle.dump(tfidf_transformer, open("tfidf.pkl", "wb"))
(0, 2308) 0.07964818452835622 (0, 2269) 0.07120940477561208 (0, 2252) 0.1386527618277715 (0, 2207) 0.12867555490594293 (0, 2195) 0.22323881403385415 (0, 2169) 0.08199346496469591 (0, 2117) 0.277305523655543 (0, 2092) 0.07790243807018668 (0, 2054) 0.1251840732786807 (0, 1929) 0.1386527618277715 (0, 1925) 0.09904961285371179 (0, 1862) 0.0859925390055646 (0, 1789) 0.1386527618277715 (0, 1787) 0.1386527618277715 (0, 1774) 0.12867555490594293 (0, 1727) 0.12159661393875565 (0, 1645) 0.277305523655543 (0, 1591) 0.10164220009509845 (0, 1525) 0.12867555490594293 (0, 1442) 0.2090129328195829 (0, 1414) 0.12217635542343386 (0, 1407) 0.11610576074272766 (0, 1406) 0.1386527618277715 (0, 1404) 0.10454046604973977 (0, 1251) 0.1386527618277715 : : (203, 12) 0.20804753030659778 (204, 2310) 0.286485880978558 (204, 2308) 0.3912372090109013 (204, 2269) 0.11659511950256453 (204, 2240) 0.4213752873961798 (204, 2143) 0.19909689988241525 (204, 2092) 0.06377699760976885 (204, 2079) 0.2685049222392095 (204, 2056) 0.05731677378035633 (204, 2054) 0.10248542330706564 (204, 2008) 0.19909689988241525 (204, 1612) 0.17116992018584712 (204, 1414) 0.06668204762124239 (204, 1365) 0.14584320493527944 (204, 1239) 0.1583393827002019 (204, 1158) 0.19909689988241525 (204, 1116) 0.2106876436980899 (204, 963) 0.13631907385511938 (204, 857) 0.22702387957898335 (204, 856) 0.17116992018584712 (204, 757) 0.19909689988241525 (204, 390) 0.22702387957898335 (204, 331) 0.11659511950256453 (204, 319) 0.10533061321927442 (204, 185) 0.1583393827002019
In [79]:
x_train, x_test, y_train, y_test = train_test_split(X_train_tfidf, dataset.flag, test_size=0.2, random_state=42)
Validate Accuracy using different algorithms¶
In [80]:
results = []
names = []
Naive Bayes Algorithm¶
In [81]:
# Naive Bayes Algorithm
clf = MultinomialNB().fit(x_train, y_train)
y_predicted = clf.predict(x_test)
acc = (metrics.accuracy_score(y_test, y_predicted))*100
results.append(acc)
names.append('NBA')
print("Test Data Accuracy Naive Bayes Algorithm: "+str(acc))
Test Data Accuracy Naive Bayes Algorithm: 85.36585365853658
In [82]:
matrix = metrics.confusion_matrix(y_test,y_predicted)
print('Confusion matrix : \n', matrix)
Confusion matrix : [[6 0 0 0 0] [0 8 1 0 0] [0 0 8 0 0] [1 0 3 4 1] [0 0 0 0 9]]
In [83]:
new_doc = "Advantage of density-based clustering over k means"
new_doc = [new_doc]
loaded_vec = CountVectorizer(vocabulary=pickle.load(open("vec_count.pkl", "rb")))
loaded_tfidf = pickle.load(open("tfidf.pkl","rb"))
x_new_counts = loaded_vec.transform(new_doc)
x_new_tfidf = loaded_tfidf.transform(x_new_counts)
predict = clf.predict(x_new_tfidf)
print(predict)
['IT4060']
In [84]:
result_bayes = pd.DataFrame({'true_labels': y_test, "predicted_labels": y_predicted})
print(result_bayes)
true_labels predicted_labels 15 IT4060 IT4060 9 IT4060 IT4060 100 SE4010 SE4010 132 SE1234 Notice 68 IT4130 IT4130 95 SE4010 SE4010 159 SE1234 SE4010 162 SE1234 Notice 147 SE1234 SE1234 182 Notice Notice 191 Notice Notice 164 Notice Notice 65 IT4130 IT4130 175 Notice Notice 73 IT4130 IT4130 152 SE1234 Notice 18 IT4060 IT4060 82 SE4010 SE4010 86 SE4010 SE4010 143 SE1234 SE1234 60 IT4130 IT4130 101 SE4010 SE4010 98 SE4010 SE4010 30 IT4060 IT4060 25 IT4060 IT4060 16 IT4060 IT4060 168 Notice Notice 195 Notice Notice 97 SE4010 SE4010 194 Notice Notice 67 IT4130 Notice 120 SE4010 SE4010 154 SE1234 SE1234 202 Notice Notice 79 IT4130 IT4130 69 IT4130 IT4130 145 SE1234 IT4060 55 IT4130 IT4130 45 IT4130 IT4130 84 SE4010 SE4010 146 SE1234 SE1234
Support Vector Machine¶
In [85]:
# SVM
clf_svm = svm.LinearSVC()
clf_svm.fit(x_train, y_train)
y_predicted_svm = clf_svm.predict(x_test)
acc = (metrics.accuracy_score(y_test, y_predicted_svm))*100
results.append(acc)
names.append('SVM')
print("Test Data Accuracy SVM Algorithm: "+str(acc))
Test Data Accuracy SVM Algorithm: 92.6829268292683
In [86]:
new_doc = "Secure software development violations threats faults"
new_doc = [new_doc]
loaded_vec = CountVectorizer(vocabulary=pickle.load(open("vec_count.pkl", "rb")))
loaded_tfidf = pickle.load(open("tfidf.pkl","rb"))
x_new_counts = loaded_vec.transform(new_doc)
x_new_tfidf = loaded_tfidf.transform(x_new_counts)
predict = clf_svm.predict(x_new_tfidf)
print(predict)
['SE1234']
In [87]:
result_bayes = pd.DataFrame({'true_labels': y_test, "predicted_labels": y_predicted})
print(result_bayes)
true_labels predicted_labels 15 IT4060 IT4060 9 IT4060 IT4060 100 SE4010 SE4010 132 SE1234 Notice 68 IT4130 IT4130 95 SE4010 SE4010 159 SE1234 SE4010 162 SE1234 Notice 147 SE1234 SE1234 182 Notice Notice 191 Notice Notice 164 Notice Notice 65 IT4130 IT4130 175 Notice Notice 73 IT4130 IT4130 152 SE1234 Notice 18 IT4060 IT4060 82 SE4010 SE4010 86 SE4010 SE4010 143 SE1234 SE1234 60 IT4130 IT4130 101 SE4010 SE4010 98 SE4010 SE4010 30 IT4060 IT4060 25 IT4060 IT4060 16 IT4060 IT4060 168 Notice Notice 195 Notice Notice 97 SE4010 SE4010 194 Notice Notice 67 IT4130 Notice 120 SE4010 SE4010 154 SE1234 SE1234 202 Notice Notice 79 IT4130 IT4130 69 IT4130 IT4130 145 SE1234 IT4060 55 IT4130 IT4130 45 IT4130 IT4130 84 SE4010 SE4010 146 SE1234 SE1234
Multi-layer Perceptron classifier¶
In [88]:
# MLP Classifier
clf_mlp = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(15,), random_state=1)
clf_mlp.fit(x_train, y_train)
y_predicted_neural = clf_mlp.predict(x_test)
acc = (metrics.accuracy_score(y_test, y_predicted_neural))*100
results.append(acc)
names.append('MLP')
print("Test Data Accuracy MLP Classifier: "+str(acc))
Test Data Accuracy MLP Classifier: 85.36585365853658
In [89]:
new_doc = "Advantage of density-based clustering over k means"
new_doc = [new_doc]
loaded_vec = CountVectorizer(vocabulary=pickle.load(open("vec_count.pkl", "rb")))
loaded_tfidf = pickle.load(open("tfidf.pkl","rb"))
x_new_counts = loaded_vec.transform(new_doc)
x_new_tfidf = loaded_tfidf.transform(x_new_counts)
predict = clf_mlp.predict(x_new_tfidf)
print(predict)
['IT4060']
Plot the results¶
In [90]:
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
classifier = names
accuracy = results
ax.bar(classifier,accuracy)
plt.show()
Save the model¶
In [91]:
# SVM
filename = 'finalized_model.pickle'
pickle.dump(clf_svm, open(filename, 'wb'))
Load the model¶
In [92]:
filename = 'finalized_model.pickle'
loaded_model = pickle.load(open(filename, 'rb'))
new_doc ="Advantage of density-based clustering over k means"
new_doc = [new_doc]
loaded_vec = CountVectorizer(vocabulary=pickle.load(open("vec_count.pkl", "rb")))
loaded_tfidf = pickle.load(open("tfidf.pkl","rb"))
x_new_counts = loaded_vec.transform(new_doc)
x_new_tfidf = loaded_tfidf.transform(x_new_counts)
predict = loaded_model.predict(x_new_tfidf)
print(predict)
['IT4060']
In [ ]: