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@@ -25,10 +25,15 @@ def predict(src2month):
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pkg_num = len(src2month)
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training_num = len(src2month['linux'])
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+ trainXdict = dict()
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+ trainYdict = dict()
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+ testXdict = dict()
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+ testYdict = dict()
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+
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look_back = 4
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# create the LSTM network
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model = Sequential()
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- model.add(LSTM(64, input_dim=look_back, activation ='relu', dropout_W =0.1, dropout_U =0.1))
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+ model.add(LSTM(32, input_dim=look_back, activation ='relu', dropout_W =0.1, dropout_U =0.1))
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# model.add(Dense(12, init='uniform', activation='relu'))
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# model.add(Dense(8, init='uniform', activation='relu'))
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# model.add(Dense(1, init='uniform', activation='sigmoid'))
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@@ -40,9 +45,52 @@ def predict(src2month):
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flag = True
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###################################################################################################
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- for pkg_name in ['icedove', 'linux', 'mysql', 'xulrunner', 'wireshark', 'firefox', 'openjdk', 'php5', 'iceape', 'wordpress', 'xen', 'openssl', 'chromium-browser']:
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-# for pkg_name in ['chromium-browser']:
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+ for pkg_name in src2month:
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+# for pkg_name in ['icedove', 'mysql', 'xulrunner', 'wireshark', 'firefox', 'openjdk', 'php5', 'iceape', 'wordpress', 'xen', 'openssl', 'chromium-browser', 'linux']:
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+# for pkg_name in ['linux']:
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pkg_num = len(src2month)
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+ dataset = src2month[pkg_name]
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+
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+ if sum(dataset)>20:
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+
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+ dataset = pandas.rolling_mean(dataset, window=12)
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+ dataset = dataset[12:]
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+
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+ # normalize the dataset
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+ dataset = scaler.fit_transform(dataset)
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+
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+ train_size = int(len(dataset) * 0.80)
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+ test_size = len(dataset) - train_size
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+ train, test = dataset[0:train_size], dataset[train_size:len(dataset)]
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+ print(len(train), len(test))
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+
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+ # reshape into X=t and Y=t+1
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+ trainX, trainY = create_dataset(train, look_back)
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+ testX, testY = create_dataset(test, look_back)
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+
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+ # reshape input to be [samples, time steps, features]
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+ trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
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+ testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
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+
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+ # save to dict for later
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+ trainXdict[pkg_name], trainYdict[pkg_name] = trainX, trainY
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+ testXdict[pkg_name], testYdict[pkg_name] = testX, testY
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+
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+ # fit the LSTM network
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+ model.fit(trainX, trainY, nb_epoch=10, batch_size=1, verbose=2)
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+
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+
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+###################################################################################################
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+
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+
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+ model.save('all_packages_test.h5')
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+
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+
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+ for pkg_name in ['icedove', 'mysql', 'xulrunner', 'wireshark', 'firefox', 'openjdk', 'php5', 'iceape', 'wordpress', 'xen', 'openssl', 'chromium-browser', 'linux']:
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+
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+ trainX, trainY = trainXdict[pkg_name], trainYdict[pkg_name]
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+ testX, testY = testXdict[pkg_name], testYdict[pkg_name]
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+
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dataset = src2month[pkg_name]
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dataset = pandas.rolling_mean(dataset, window=12)
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dataset = dataset[12:]
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@@ -50,52 +98,34 @@ def predict(src2month):
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# normalize the dataset
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dataset = scaler.fit_transform(dataset)
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- train_size = int(len(dataset) * 0.80)
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- test_size = len(dataset) - train_size
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- train, test = dataset[0:train_size], dataset[train_size:len(dataset)]
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- print(len(train), len(test))
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- # reshape into X=t and Y=t+1
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- trainX, trainY = create_dataset(train, look_back)
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- testX, testY = create_dataset(test, look_back)
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-
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- # reshape input to be [samples, time steps, features]
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- trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
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- testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
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- # fit the LSTM network
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- model.fit(trainX, trainY, nb_epoch=5, batch_size=1, verbose=2)
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-
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-
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-###################################################################################################
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+ # make predictions
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+ trainPredict = model.predict(trainX)
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+ testPredict = model.predict(testX)
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+ # invert predictions
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+ trainPredict = scaler.inverse_transform(trainPredict)
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+ trainY = scaler.inverse_transform([trainY])
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+ testPredict = scaler.inverse_transform(testPredict)
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+ testY = scaler.inverse_transform([testY])
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+ # calculate root mean squared error
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+ print('Package: ' + pkg_name)
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+ trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
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+ print('Train Score: %.2f RMSE' % (trainScore))
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+ testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
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+ print('Test Score: %.2f RMSE' % (testScore))
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- # make predictions
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- trainPredict = model.predict(trainX)
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- testPredict = model.predict(testX)
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- print(type(testPredict))
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- # invert predictions
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- trainPredict = scaler.inverse_transform(trainPredict)
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- trainY = scaler.inverse_transform([trainY])
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- testPredict = scaler.inverse_transform(testPredict)
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- testY = scaler.inverse_transform([testY])
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- # calculate root mean squared error
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- trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
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- print('Train Score: %.2f RMSE' % (trainScore))
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- testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
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- print('Test Score: %.2f RMSE' % (testScore))
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-
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-
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- # shift train predictions for plotting
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- trainPredictPlot = numpy.empty_like(dataset)
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- trainPredictPlot[:] = numpy.nan
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- trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict[:, 0]
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- # shift test predictions for plotting
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- testPredictPlot = numpy.empty_like(dataset)
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- testPredictPlot[:] = numpy.nan
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- testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1] = testPredict[:, 0]
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- # plot baseline and predictions
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- plt.plot(scaler.inverse_transform(dataset))
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- plt.plot(trainPredictPlot)
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- plt.plot(testPredictPlot)
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- plt.show()
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+ # shift train predictions for plotting
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+ trainPredictPlot = numpy.empty_like(dataset)
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+ trainPredictPlot[:] = numpy.nan
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+ trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict[:, 0]
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+ # shift test predictions for plotting
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+ testPredictPlot = numpy.empty_like(dataset)
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+ testPredictPlot[:] = numpy.nan
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+ testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1] = testPredict[:, 0]
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+ # plot baseline and predictions
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+ plt.plot(scaler.inverse_transform(dataset))
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+ plt.plot(trainPredictPlot)
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+ plt.plot(testPredictPlot)
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+ plt.show()
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