lstm

Lstm.py

@@ -0,0 +1,491 @@
+import numpy
+import matplotlib.pyplot as plt
+import pandas
+import math
+from keras import backend as K
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+from keras.layers import Activation, Dropout
+from keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_squared_error
+
+numpy.random.seed(7)
+
+
+# convert an array of values into a dataset matrix
+# ATTENTION: THIS FUNCTION CHANGES SIZE OF INPUT
+def create_dataset(original_dataset, dataset, meta, num_steps, look_back=1):
+    dataX, dataY = [], []
+    for i in range(len(dataset) - look_back - num_steps):
+        a = []
+        for j in range(i, i + look_back):
+            # a.append([dataset[j]] + meta)
+            a.append([dataset[j]])
+        dataX.append(a)
+        mean = 0
+        for j in range(num_steps):
+            mean += original_dataset[i + look_back + j]
+
+        dataY.append(mean / num_steps)
+    return numpy.array(dataX), numpy.array(dataY)
+
+
+## Calculate weighted average for comparison
+def calc_waverage(raw_av, lamda_w):
+    w_average = 0
+    weights = 0
+
+    print(raw_av)
+    if (raw_av.size == 0):
+        w_average = 0
+        return w_average
+    else:
+        jj = 0
+        for j in raw_av:
+            w_average += j * math.exp(-(len(raw_av) - jj - 1) / lamda_w)
+            weights += math.exp(-(len(raw_av) - jj - 1) / lamda_w)
+            jj += 1
+
+    try:
+        w_average = w_average / weights
+    except ZeroDivisionError:
+        print('Error:', raw_av)
+    return w_average
+
+
+def normalizer(src2month, pkg, smoothing, num_steps):
+    time_series = numpy.array(src2month[pkg])
+    time_series = pandas.rolling_mean(time_series, window=smoothing)
+    time_series = time_series[smoothing:]
+    # print(len(time_series))
+    # print(time_series)
+
+    i = 0
+    for month in time_series:
+        if (numpy.isclose(month, 0)):
+            # print(str(month))
+            i += 1
+        else:
+            break
+
+    # print(str(i))
+
+    try:
+        max_value = numpy.amax(time_series[i:-12]) * 9
+        min_value = numpy.amin(time_series[i:-12]) * 9
+    except ValueError:
+        max_value = 0
+        min_value = 0
+
+    norm = max_value - min_value
+    if (norm < 1):
+        norm = 1
+
+    return (norm)
+
+
+def calcf(fvalues, src2month):
+    vulns20152016 = dict()
+    total_top = 0
+    number_top = 20
+    total_low = 0
+    number_low = 0
+
+    for pkg in src2month:
+        vulns20152016[pkg] = sum(src2month[pkg][-36:-12])
+
+    vulnslist = vulns20152016.items()
+
+    newlist = sorted(vulnslist, key=lambda k: k[1], reverse=True)
+    cutoff_value = newlist[number_top][1]
+    print('Cutoff value: ' + str(cutoff_value))
+
+    for pkg in vulns20152016:
+        if vulns20152016[pkg] > cutoff_value:
+            total_top += vulns20152016[pkg]
+        elif vulns20152016[pkg] < cutoff_value and vulns20152016[pkg] > 0:
+            total_low += vulns20152016[pkg]
+            number_low += 1
+
+    average_top_9month = (total_top / number_top) * 9 / 24
+    r_top = 1.05 * (average_top_9month / (9 * 30 * 4))
+    average_low_9month = (total_low / number_low) * 9 / 24
+    r_low = 1.05 * (average_low_9month / (9 * 30 * 4))
+
+    for pkg in vulns20152016:
+        if vulns20152016[pkg] > cutoff_value:
+            fvalues[pkg] = 1 - r_top
+        else:
+            fvalues[pkg] = 1 - r_low
+
+
+def test_model(pkg_name, src2month, model_file, totalX, totalY, scaler, num_steps, smoothing, batch_num, lamda_w,
+               reality_list, prediction_lstm, prediction_ave, prediction_wave, prediction_last):
+    model = load_model(
+        './models/' + pkg_name + '-' + str(num_steps) + 'smoothing' + str(smoothing) + str(model_file) + '.h5')
+    model.reset_states()
+    totalPredict = model.predict(totalX[18:], batch_size=batch_num)
+    # model.reset_states()
+
+    totalPredict = scaler.inverse_transform(totalPredict)
+    totalPredict = totalPredict.flatten()
+    totalY = totalY[18:]
+    totalY = totalY.reshape(-1, 1)
+    totalY = scaler.inverse_transform(totalY)
+    totalY = totalY.flatten()
+
+    trainPredict = totalPredict[:-10]
+    evaluatePredict = totalPredict[-10]
+    testPredict = totalPredict[-1]
+    model.reset_states()
+
+    evaluation = evaluatePredict * 9
+    if (evaluation < 0):
+        evaluation = 0
+    prediction = testPredict * 9
+    if (prediction < 0):
+        prediction = 0
+
+    evaluation_reality = sum(src2month[pkg_name][-21:-12])
+    reality = sum(src2month[pkg_name][-12:-3])
+    if (reality == 0):
+        normalizer = 1
+    else:
+        normalizer = reality
+
+    evaluationerror = evaluation_reality - evaluation
+
+    testerror = (reality - prediction) / normalizer
+    print('#' * 80)
+    print(pkg_name)
+    print('prediction: ' + str(prediction))
+    print('reality: ' + str(totalY[-1] * 9) + ' = ' + str(reality))
+    print('Normalized error: ' + str(testerror))
+    print('Validation error: ' + str(evaluationerror))
+
+    # Plot
+    #plt.plot(totalY, color='blue')
+    #plt.plot(totalPredict, color='red')
+    #plt.show()
+
+    ## Calculate average for comparison
+    raw_av = numpy.array(src2month[pkg_name])
+    i = 0
+    max_value = 0
+    min_value = 0
+    for month in raw_av:
+        if (month == 0):
+            i += 1
+
+        average = sum(raw_av[i:-13]) / len(raw_av[i:-13])
+        average_error = (reality - average) / normalizer
+        w_average = calc_waverage(raw_av[i:-13], lamda_w)
+        w_average_error = (reality - w_average) / normalizer
+
+        last = sum(raw_av[-22:-13])
+        last_error = (reality - last) / normalizer
+
+        print(average * 9)
+        print(w_average * 9)
+        print(last)
+        print('#' * 80)
+
+        reality_list.append(reality)
+        prediction_lstm.append(prediction)
+        prediction_ave.append(average)
+        prediction_wave.append(w_average)
+        prediction_last.append(last)
+        # if(not numpy.isinf(testerror)):
+        #    if(testerror>1):
+        #        testerror=1.0
+        #    if(average_error>1):
+        #        average_error=1.0
+        #    if(w_average_error>1):
+        #        w_average_error=1.0
+        #    if(last_error>1):
+        #        last_error=1.0
+        #    total_error += numpy.absolute(testerror)
+        #    total_ave_error += numpy.absolute(average_error)
+        #    total_wave_error += numpy.absolute(w_average_error)
+        #    total_last_error += numpy.absolute(last_error)
+        #    num_packages += 1
+
+        return (prediction, reality, testerror, evaluationerror, evaluation, evaluation_reality)
+
+
+def predict(src2month, src2sloccount, src2pop, src2deps):
+    ## Number of features
+    feat_num = 1
+
+    ## Model parameters
+    do_train = True
+    do_test = True
+    models_num = 5
+    num_steps = 9
+    smoothing = num_steps
+    num_neurons = 10
+    look_back = 3
+    lamda_w = 12
+    init_test_size = 18
+
+    pkg_num = len(src2month)
+
+    trainXdict = dict()
+    trainYdict = dict()
+    testXdict = dict()
+    testYdict = dict()
+
+    train_size = int(len(src2month['linux']) - init_test_size)
+    test_size = len(src2month['linux']) - train_size
+    batch_num = train_size - num_steps - look_back - smoothing - 2
+    print("batch_num:")
+    print(batch_num)
+
+    # create the LSTM network
+    model = Sequential()
+    model.add(LSTM(num_neurons, batch_input_shape=(batch_num, look_back, feat_num), activation='relu', dropout=0.5,
+                   stateful=True))
+    #    model.add((keras.layers.0, recurrent_dropout=0.4, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=True, unroll=False))
+    #    model.add(Dense(32, activation='relu'))
+    #    model.add(Dense(16, activation='relu'))
+    model.add(Dense(1))
+    model.compile(loss='mean_squared_error', optimizer='adam')
+    Wsave = model.get_weights()
+
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    # scaler2 = MinMaxScaler(feature_range=(0,1))
+    # scaler3 = MinMaxScaler(feature_range=(0,1))
+
+    test_scale = []
+    #    for i in src2month:
+    #        test_scale = numpy.concatenate((test_scale, src2month[i]))
+    #        for j in src2month[i]:
+    #            test_scale.append(src2month[i][j])
+
+    # test_scale = []
+    # for i in src2pop:
+    #    test_scale.append(src2pop[i][1])
+
+    # scaler2.fit(test_scale)
+
+    # test_scale = []
+
+    # for i in src2sloccount:
+    #    test_scale.append(src2sloccount[i][0])
+
+    # scaler3.fit(test_scale)
+
+    total_trainX = []
+    total_trainY = []
+
+    flag = True
+    ###################################################################################################
+    for i in range(models_num):
+
+        #        for pkg_name in ['chromium-browser', 'firefox-esr', 'linux']:
+        for pkg_name in src2month:
+            pkg_num = len(src2month)
+
+            dataset = numpy.array(src2month[pkg_name])
+            dataset = dataset[:len(dataset) - 2]
+            dataset = dataset.reshape(-1, 1)
+            scaler.fit(dataset)
+            dataset = dataset.flatten()
+            #
+            original_dataset = dataset
+            dataset = pandas.rolling_mean(dataset, window=smoothing)
+            original_dataset = original_dataset[smoothing:]
+            dataset = dataset[smoothing:]
+            dataset = dataset.reshape(-1, 1)
+            dataset = scaler.transform(dataset)
+            dataset = dataset.flatten()
+
+            total_sum = sum(original_dataset)
+            original_dataset = original_dataset.reshape(-1, 1)
+            original_dataset = scaler.transform(original_dataset)
+            original_dataset = original_dataset.flatten()
+
+            #print(dataset.shape)
+            #print(len(dataset))
+
+            if (total_sum > 80):
+                #               reset or not between training
+                model.set_weights(Wsave)
+                ## ommit for rolling mean
+                # normalize the dataset
+
+                train_size = len(dataset) - init_test_size
+                test_size = len(dataset) - train_size
+                train_original, test_original = original_dataset[0:train_size], original_dataset[
+                                                                                train_size:len(dataset)]
+                train, test = dataset[0:train_size], dataset[train_size:len(dataset)]
+                print(len(train), len(test))
+
+                # get metadata
+                meta = []
+                # try:
+                #    pop_vote = src2pop[pkg_name][1]
+                # except KeyError:
+                #    pop_vote = 0
+
+                # try:
+                #    slocs_total = src2sloccount[pkg_name][0]
+                # except KeyError:
+                #    slocs_total = 0
+
+                # pop_vote = scaler2.transform([[pop_vote]])
+                # slocs_total = scaler3.transform([[slocs_total]])
+
+                # meta.append(pop_vote)
+                # meta.append(slocs_total)
+
+                # reshape into X=t and Y=t+1
+                trainX, trainY = create_dataset(train_original, train, meta, num_steps, look_back)
+                testX, testY = create_dataset(test_original, test, meta, num_steps, look_back)
+
+                # reshape input to be [samples, time steps, features]
+                trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], feat_num))
+                testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], feat_num))
+
+                trainY.reshape(-1, 1)
+                testY.reshape(-1, 1)
+
+                # fit the LSTM network
+                if do_train:
+                    for j in range(100):
+                        model.fit(trainX, trainY, nb_epoch=1, batch_size=len(trainX), verbose=2, shuffle=False)
+                        model.reset_states()
+                    try:
+                        model.save('./models/' + pkg_name + '-' + str(num_steps) + 'smoothing' + str(smoothing) + str(
+                            i) + '.h5')
+                    except OSError:
+                        model.save('./models/unknown-' + str(num_steps) + 'smoothing' + str(smoothing) + '.h5')
+                # else:
+                #    try:
+                #        model.save('./moels/low_together' + '-' + str(num_steps) + 'smoothing' + str(smoothing) + '.h5')
+                #    except OSError:
+                #        model.save('./models/unknown-' + str(num_steps) + 'smoothing' + str(smoothing) + '.h5')
+
+    #   model.save('all_packages_test'+str(num_steps)+ '-' + str(feat_num) + '.h5')
+    #   model = load_model('all_packages_test'+str(num_steps)+ '-' + str(feat_num) + '.h5')
+
+    ###################################################################################################
+
+    #    target = open('output-Errors-ALLPACKAGES-NEW' + str(num_steps) + 'smoothing' + str(smoothing) + 'neurons' + str(num_neurons) + '.txt','w')
+    target2 = open('results_paper' + str(num_steps) + '.txt', 'w')
+
+    #    for pkg_name in ['chromium-browser', 'firefox-esr', 'linux']:
+    #    for pkg_name in ['libpng']:
+    reality_list = []
+    prediction_lstm = []
+    prediction_ave = []
+    prediction_wave = []
+    prediction_last = []
+    num_packages = 0
+    select_best = True
+
+    fvalues = dict()
+
+    calcf(fvalues, src2month)
+
+    for pkg_name in src2month:
+
+        dataset = numpy.array(src2month[pkg_name])
+        dataset = dataset[:len(dataset) - 2]
+        dataset = dataset.reshape(-1, 1)
+        scaler.fit(dataset)
+        dataset = dataset.flatten()
+        #
+        original_dataset = dataset
+        dataset = pandas.rolling_mean(dataset, window=smoothing)
+        original_dataset = original_dataset[smoothing:]
+        dataset = dataset[smoothing:]
+        dataset = dataset.reshape(-1, 1)
+        dataset = scaler.transform(dataset)
+        dataset = dataset.flatten()
+
+        total_sum = sum(original_dataset)
+
+        original_dataset = original_dataset.reshape(-1, 1)
+        original_dataset = scaler.transform(original_dataset)
+        original_dataset = original_dataset.flatten()
+
+        if (total_sum > 100 and do_test):
+
+            best_model = 0
+            best_error = 100.0
+            totalX, totalY = create_dataset(original_dataset, dataset, meta, num_steps, look_back)
+
+            if select_best:
+                for i in range(5):
+
+                    (prediction, reality, testerror, evaluationerror, evaluation, evaluation_reality) = test_model(
+                        pkg_name, src2month, i, totalX, totalY, scaler, num_steps, smoothing, batch_num, lamda_w,
+                        reality_list, prediction_lstm, prediction_ave, prediction_wave, prediction_last)
+                    if (evaluationerror < best_error):
+                        best_model = i
+                        best_error = evaluationerror
+
+                model = load_model('./models/' + pkg_name + '-' + str(num_steps) + 'smoothing' + str(smoothing) + str(
+                    best_model) + '.h5')
+                model.save(
+                    './models/' + pkg_name + '-' + str(num_steps) + 'smoothing' + str(smoothing) + 'best' + '.h5')
+                K.clear_session()
+
+            (prediction, reality, testerror, evaluationerror, evaluation, evaluation_reality) = test_model(pkg_name,
+                                                                                                           src2month,
+                                                                                                           'best',
+                                                                                                           totalX,
+                                                                                                           totalY,
+                                                                                                           scaler,
+                                                                                                           num_steps,
+                                                                                                           smoothing,
+                                                                                                           batch_num,
+                                                                                                           lamda_w,
+                                                                                                           reality_list,
+                                                                                                           prediction_lstm,
+                                                                                                           prediction_ave,
+                                                                                                           prediction_wave,
+                                                                                                           prediction_last)
+            normalizer_value = normalizer(src2month, pkg_name, smoothing, num_steps)
+            certainty = 1 - numpy.absolute(evaluationerror / normalizer_value)
+            if (certainty < 0.1):
+                certainty = 0.1
+
+            print(str(normalizer_value))
+
+            # Plot
+            # plt.plot(totalY, color='blue')
+            # plt.plot(totalPredict, color='red')
+            # plt.show()
+
+            # need pkg_name, prediction, certainity, fvalues
+            # TODO: save in form packageName:prediction:errorComplement:initial_expectation
+            target2.write(pkg_name + ':' + str(prediction) + ':' + str(certainty) + ':' + str(fvalues[pkg_name]) + '\n')
+            K.clear_session()
+        else:
+            raw_av = src2month[pkg_name]
+            reality = sum(src2month[pkg_name][-12:-3])
+            i = 0
+            max_value = 0
+            min_value = 0
+            for month in raw_av:
+                if (month == 0):
+                    i += 1
+
+            w_average = calc_waverage(numpy.array(raw_av[i:-13]), lamda_w)
+            normalizer_value = normalizer(src2month, pkg_name, smoothing, num_steps)
+            certainty = 0.95
+            # TODO: save in form packageName:prediction:errorComplement:initial_expectation
+            target2.write(pkg_name + ':' + str(w_average) + ':' + str(certainty) + ':' + str(fvalues[pkg_name]) + '\n')
+
+    mean_error = math.sqrt(mean_squared_error(prediction_lstm, reality_list))
+    mean_ave_error = math.sqrt(mean_squared_error(prediction_ave, reality_list))
+    mean_wave_error = math.sqrt(mean_squared_error(prediction_wave, reality_list))
+    mean_last_error = math.sqrt(mean_squared_error(prediction_last, reality_list))
+    print(mean_error)
+    print(mean_ave_error)
+    print(mean_wave_error)
+    print(mean_last_error)

M-star.py

@@ -2,6 +2,7 @@ import logging
 import sys
 
 from vendors.debian.DebianModel import DebianModel
+import Lstm as lstm
 
 ##### GLOBAL VARIABLES #####
 logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
@@ -27,7 +28,7 @@ def __main__(configfile='config_default.txt', vendorname='debian', action='help'
     if vendorname is 'debian':
         model = DebianModel(action)
 
-        model.performTests()
+        lstm.predict(model.get_src2month(),{},{},{})
 
         #for norm_param in range(1, 15):
         # get model as set of opinions

results_paper9.txt

@@ -0,0 +1,1258 @@
+qt-x11-free:0:0.95:0.9987826359084406
+c-icap:0:0.95:0.9987826359084406
+axel:0:0.95:0.9987826359084406
+devscripts:0:0.95:0.9987826359084406
+tryton-client:0:0.95:0.9987826359084406
+flightgear:0:0.95:0.9987826359084406
+mercurial:0:0.95:0.9987826359084406
+libapache-mod-auth-kerb:0:0.95:0.9987826359084406
+c-ares:0:0.95:0.9987826359084406
+gmime2.2:0:0.95:0.9987826359084406
+mozart:0:0.95:0.9987826359084406
+mysql:0.0:0.95:0.9987826359084406
+libvorbis:0:0.95:0.9987826359084406
+gtksee:0:0.95:0.9987826359084406
+ircd-hybrid:0:0.95:0.9987826359084406
+libxfixes:0:0.95:0.9987826359084406
+virtualbox:0:0.95:0.9987826359084406
+icu:0.66743693418:0.95:0.9987826359084406
+netkit-telnet:0:0.95:0.9987826359084406
+bcfg2:0:0.95:0.9987826359084406
+miniupnpc:0:0.95:0.9987826359084406
+lucene-solr:0:0.95:0.9987826359084406
+terminology:0:0.95:0.9987826359084406
+proftpd:0:0.95:0.9987826359084406
+storebackup:0:0.95:0.9987826359084406
+mailx:0:0.95:0.9987826359084406
+up-imapproxy:0:0.95:0.9987826359084406
+mgetty:0:0.95:0.9987826359084406
+xulrunner:8.06323903799:0.856013588607:0.9987826359084406
+tomcat8:1.00200415488:0.95:0.9987826359084406
+spice:0:0.95:0.9987826359084406
+libdbd-pg-perl:0:0.95:0.9987826359084406
+evolution:0:0.95:0.9987826359084406
+ucd-snmp:0:0.95:0.9987826359084406
+quassel:0:0.95:0.9987826359084406
+logwatch:0:0.95:0.9987826359084406
+faqomatic:0:0.95:0.9987826359084406
+libfs:0:0.95:0.9987826359084406
+arj:0:0.95:0.9987826359084406
+zebra:0:0.95:0.9987826359084406
+ecryptfs-utils:0:0.95:0.9987826359084406
+netpbm-free:0:0.95:0.9987826359084406
+libffi:0:0.95:0.9987826359084406
+courier:0:0.95:0.9987826359084406
+libpng1.6:0.0:0.95:0.9987826359084406
+lukemftpd:0:0.95:0.9987826359084406
+canna:0:0.95:0.9987826359084406
+libast2:0:0.95:0.9987826359084406
+freeamp:0:0.95:0.9987826359084406
+libhtml-parser-perl:0:0.95:0.9987826359084406
+libcurl3-gnutls:0:0.95:0.9987826359084406
+openswan:0:0.95:0.9987826359084406
+sendmail:0:0.95:0.9987826359084406
+ncompress:0:0.95:0.9987826359084406
+libreoffice:0.371715602702:0.95:0.9987826359084406
+libapache2-mod-rpaf:0:0.95:0.9987826359084406
+tidy:0:0.95:0.9987826359084406
+gsambad:0:0.95:0.9987826359084406
+glibc:0.293965966622:0.95:0.9987826359084406
+elog:0:0.95:0.9987826359084406
+tuxpaint:0:0.95:0.9987826359084406
+ircd-ratbox:0:0.95:0.9987826359084406
+parcimonie:0:0.95:0.9987826359084406
+turqstat:0:0.95:0.9987826359084406
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vendors/debian/Tests.py

@@ -132,13 +132,15 @@ class Tests:
 
             real_rel_prediction = Tests.get_src2month_data(subset1, model, months) / Tests.get_src2month_data(subset2, model, months)
             real_rel_list.append(real_rel_prediction)
+            print("Computed relativity : " + str ( computed_rel_prediction ) + " : "+ str(real_rel_prediction))
 
         # at this point we have two lists of computed relatives, lets see how similair are they
         similarities = []
         for i in range(0, 100):
-            similarity = abs(real_rel_list[i]-computetd_rel_list[i])/real_rel_list[i]
+            similarity = abs(real_rel_list[i]-computetd_rel_list[i])
             similarities.append(similarity)
 
+        print(similarities)
         avg_normal_relativity = sum(similarities)/len(similarities)
         avg_quadratic_relativity = math.sqrt(sum(math.pow(i, 2) for i in similarities) / len(similarities))
         print("Average normal relativity: " + str(avg_normal_relativity))