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visualizer.py
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visualizer.py
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import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import numpy as np
import pandas as pd
import imgkit
def CorrelationMatrixSave(df):
# Set CSS properties for table elements in dataframe
table_props = [
('border-style', 'solid lightgrey'),
('border-color', 'lightgrey'),
('border-spacing', '0px'),
('border-width', '0px 1px 1px 0px')
]
# Set CSS properties for th elements in dataframe
th_props = [
('font-size', '12px'),
('font-family', 'Arial'),
('text-align', 'right'),
('height', '30px'),
('border-style', 'solid lightgrey'),
('border-color', 'lightgrey'),
('border-spacing', '0px'),
('border-width', '0px 1px 1px 0px')
]
# Set CSS properties for tr elements in dataframe
tr_props = [
('font-size', '12px'),
('font-family', 'Arial'),
('text-align', 'right'),
('height', '30px'),
('border-style', 'solid lightgrey'),
('border-color', 'lightgrey'),
('border-spacing', '0px'),
('border-width', '0px 1px 1px 0px')
]
# Set CSS properties for td elements in dataframe
td_props = [
('font-size', '12px'),
('font-family', 'monospace'),
('text-align', 'right'),
('white-space', 'pre'),
('height', '30px'),
('line-height', 'normal'),
('border', 'none'),
('display', 'table-cell'),
('padding', '0.5em 0.5em'),
('border-style', 'solid lightgrey'),
('border-color', 'lightgrey'),
('border-spacing', '0px'),
('border-width', '0px 1px 1px 0px')
]
# Set table styles
table_styles = [
dict(selector="table", props=table_props),
dict(selector="th", props=th_props),
dict(selector="tr", props=tr_props),
dict(selector="td", props=td_props)
]
cm = df.corr().style.set_properties(**{'font-size': '12pt', 'font-family': 'monospace', 'text-align': 'right', 'white-space': 'pre'}).set_table_styles(table_styles).format("{:0.3f}").background_gradient(cmap='Blues')
hf = cm.render()
imgkit.from_string(hf, 'images/CorrelationMatrix.png')
pass
def ScatterPlots(x, y, extendedMetricNames, ylabel):
fig = plt.figure(figsize=(20,20))
row = 4; col = 2; shape = 10; fontsize = 24; labelsize = 18; color = 'r'; marker = 'o'
j = 1
for i in extendedMetricNames:
plt.subplot(row, col, j)
plt.title('Latency vs ' + i, fontsize=fontsize)
plt.scatter(x[i], y, s=shape, c=color, marker=marker)
plt.xlabel(i, fontsize=labelsize)
plt.ylabel(ylabel, fontsize=labelsize)
j = j + 1
plt.tight_layout(w_pad=15, h_pad=3)
fig.savefig('images/InnerStateVariableVsTargetVariable.jpg')
plt.show()
plt.close()
pass
def TimeLinePlot(df, x):
fig = plt.figure(figsize=(20,7))
row = 1; col = 1; fontsize = 24; labelsize = 18
tmp_mean = df[x].mean()
tmp_std = df[x].std()
plt.subplot(row, col, 1)
plt.title(x, fontsize = fontsize)
plt.plot(df[x])
plt.xlabel('Time', fontsize = labelsize)
plt.ylabel(x, fontsize = labelsize)
plt.axhline(y = tmp_mean, color = 'grey')
plt.axhline(y = tmp_mean + tmp_std, color = '0.1', linewidth = 0.8, linestyle = ':')
plt.axhline(y = tmp_mean + 2*tmp_std, color = 'grey', linewidth = 0.5, linestyle = ':')
plt.text(y = tmp_mean, x = 0, s = 'Mean', fontsize = 20)
plt.text(y = tmp_mean + tmp_std, x = 0, s = '+1Std', fontsize = 20)
plt.text(y = tmp_mean + 2*tmp_std, x = 0, s = '+2Std', fontsize = 20)
fig.savefig('images/AverageLatencyQuantileZeroPointFiveTimeLines.jpg')
plt.show()
plt.close()
pass
def TimeLinePlots(df, extendedMetricNames):
fig = plt.figure(figsize=(20,20))
row = 4; col = 2; shape = 10; fontsize = 24; labelsize = 18; color = 'r'; marker = 'o'
j = 1
for i in extendedMetricNames:
plt.subplot(row, col, j)
plt.title('Time vs ' + i, fontsize=fontsize)
# plt.plot(preProcessedDF[i], c=color, marker=marker)
plt.plot(df[i], c=color)
plt.xlabel('Time', fontsize=labelsize)
plt.ylabel(i, fontsize=labelsize)
j = j + 1
plt.tight_layout(w_pad=15, h_pad=3)
fig.savefig('images/InnerStateVariableTimeLines_2.jpg')
plt.show()
plt.close()
pass
def VisualizePredictedYScatter(y_normalized, y_predicted, targetVariable):
fontsize = 24; labelsize = 20
fig = plt.figure(figsize=(20,7))
plt.subplot(1, 1, 1)
plt.title(targetVariable + ' vs predicted ' + targetVariable + ' by Neural Network', fontsize=fontsize)
plt.scatter(x = y_normalized, y = y_predicted, s = 20)
plt.xlabel('Normalized real Response Time', fontsize=labelsize)
plt.ylabel('Normalized estimated Response Time', fontsize=labelsize)
fig.savefig('images/Y_normailizedVsY_predictedLatency.jpg')
plt.show()
plt.close()
pass
def VisualizePredictedYLine(y_normalized, y_predicted, targetVariable, lines = False):
titlesize = 24; labelsize = 20
fig = plt.figure(figsize=(20,7))
plt.subplot(1, 1, 1)
plt.title(targetVariable + ' and its predicted values', fontsize=titlesize)
plt.plot(y_normalized)
plt.plot(y_predicted)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
plt.xlabel('Time', fontsize=labelsize)
plt.ylabel('Normalized (-1,+1) latencies', fontsize=labelsize)
if( lines ):
plt.axhline(y = y_predicted.max(), color = 'grey', alpha = 0.8, linestyle = '--')
plt.axhline(y = y_predicted.min(), color = 'grey', linestyle = '--')
plt.gca().legend(('real','predicted'), loc = 2, prop={'size': 16})
fig.savefig('images/AverageLatencyQuantileZeroPointFiveTimeLinesAndPredictedValuesAgainstTime.jpg')
plt.show()
plt.close()
pass
def VisualizePredictedYLineWithValues(y_normalized, y_predicted, targetVariable, name = 'Normalized'):
titlesize = 24; labelsize = 20
fig = plt.figure(figsize=(20,7))
plt.subplot(1, 1, 1)
plt.title(targetVariable + ' and its predicted values', fontsize=titlesize)
plt.plot(y_normalized)
plt.plot(y_predicted)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
plt.xlabel('Time', fontsize=labelsize)
plt.ylabel(name + ' latencies', fontsize=labelsize)
plt.axhline(y = y_predicted.max(), color = 'grey', alpha = 0.8, linestyle = '--')
plt.axhline(y = y_predicted.min(), color = 'grey', linestyle = '--')
plt.text(y = y_predicted.max() - 0.1, x = -0, s = str(y_predicted.max())[:15], fontsize = 14, horizontalalignment='left')
plt.text(y = y_predicted.min() + 0.02, x = -0, s = str(y_predicted.min())[:15], fontsize = 14, horizontalalignment='left')
plt.gca().legend(('real','predicted'), loc = 2, prop={'size': 16})
fig.savefig('images/VisualizePredictedYLineWithValues' + name + '.jpg')
plt.show()
plt.close()
pass
def VisualizePredictedYWithWorkers(y_normalized, y_predicted, targetVariable):
titlesize = 24; labelsize = 20
fig = plt.figure(figsize=(20,7))
plt.subplot(1, 1, 1)
plt.title(targetVariable + ' and its predicted values', fontsize=titlesize)
plt.plot(y_predicted)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
plt.xlabel('Time', fontsize=labelsize)
plt.ylabel('Normalized (-1,+1) latencies', fontsize=labelsize)
variables = y_predicted.columns
plt.gca().legend((variables), loc = 2, prop={'size': 16})
fig.savefig('images/PredictedYByWorkers.jpg')
plt.show()
plt.close()
pass
def VisualizePredictedXYLine(y_normalized, y_predicted, targetVariable, lowerLimit, upperLimit):
titlesize = 24; labelsize = 20
fig = plt.figure(figsize=(20,7))
plt.subplot(1, 1, 1)
plt.title('Response time and proposed numb. of additional resources', fontsize=titlesize)
plt.plot(y_normalized)
plt.plot(y_predicted)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
plt.xlabel('Timeline', fontsize=labelsize)
plt.ylabel('Response time (ms)', fontsize=labelsize)
plt.axhline(y = lowerLimit, color = 'grey')
plt.axhline(y = upperLimit, color = 'grey')
plt.gca().legend(('Advided numb. of new resources','Measured response time (ms)'), loc = 2, prop={'size': 12})
fig.savefig('images/AverageLatencyQuantileZeroPointFiveTimeLinesAndPredictedValuesAgainstTime_2.jpg')
plt.show()
plt.close()
pass
def VisualizePredictedXY2Line(y1, y2, targetVariable, lowerLimit, upperLimit):
titlesize = 24; labelsize = 20
fig, ax1 = plt.subplots(figsize=(20,7))
# prop_cycle = plt.rcParams['axes.prop_cycle']
# colors = prop_cycle.by_key()['color']
# print(colors)
ax2 = ax1.twinx()
p1 = ax1.plot(y1, color = '#1f77b4', label = 'Response time (ms)')
p2 = ax2.plot(y2, color = '#ff7f0e', label = 'Proposedd numb. of additional resources')
ax1.set_xlabel('Timeline', fontsize = labelsize)
ax1.set_ylabel('Response time (ms)', fontsize=labelsize)
ax2.set_ylabel('Proposedd numb. of additional resources', fontsize=labelsize)
ax2.set_ylim([-2, 12])
ax1.axhline(y = lowerLimit, color = 'grey')
ax1.axhline(y = upperLimit, color = 'grey')
# ax1.text(y = lowerLimit, x = 0, s = r'{:d}'.format(lowerLimit), va = 'bottom', ha = 'center', fontsize = 12)
# ax1.text(y = upperLimit, x = 0, s = upperLimit, va = 'bottom', ha = 'center', fontsize = 12)
plt.title('Response time and proposed numb. of additional resources', fontsize=titlesize)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
ps = p1+p2
labels = [label.get_label() for label in ps]
ax1.legend(ps, labels, loc = 2, prop = {'size': 12})
fig.savefig('images/AverageLatencyQuantileZeroPointFiveTimeLinesAndPredictedValuesAgainstTime_3.jpg')
plt.show()
plt.close()
pass
def VisualizePredictedXY3Line(y1, y2, y3, targetVariable, lowerLimit, upperLimit):
titlesize = 24; labelsize = 20
fig, ax1 = plt.subplots(figsize=(20,7))
ax2 = ax1.twinx()
p1 = ax1.plot(y1, color = '#1f77b4', label = 'Response time (ms)')
p2 = ax1.plot(y2, color = '#000000', label = 'Predicted Response time (ms)')
p3 = ax2.plot(y3, color = '#ff7f0e', label = 'Proposedd numb. of additional resources')
ax1.set_xlabel('Timeline', fontsize = labelsize)
ax1.set_ylabel('Response time (ms)', fontsize=labelsize)
ax2.set_ylabel('Proposedd numb. of additional resources', fontsize=labelsize)
ax2.set_ylim([-5, 5])
ax1.axhline(y = lowerLimit, color = 'grey')
ax1.axhline(y = upperLimit, color = 'grey')
plt.title('Response time and proposed numb. of additional resources', fontsize=titlesize)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
ps = p1+p2+p3
labels = [label.get_label() for label in ps]
ax1.legend(ps, labels, loc = 2, prop = {'size': 12})
fig.savefig('images/ResponseTimeAdvicePredicted.jpg')
plt.show()
plt.close()
pass
def VisualizeDemo1(y1, y2, y3, targetVariable, lowerLimit, upperLimit, filename):
titlesize = 24; labelsize = 20
fig, ax1 = plt.subplots(figsize=(20,7))
ax2 = ax1.twinx()
p1 = ax1.plot(y1, color = '#1f77b4', label = 'Response time (ms)')
p2 = ax1.plot(y2, color = '#000000', label = 'Predicted Response time (ms)')
p3 = ax2.plot(y3, color = '#ff7f0e', label = 'Proposedd numb. of additional resources')
ax1.set_xlabel('Timeline', fontsize = labelsize)
ax1.set_ylabel('Response time (ms)', fontsize=labelsize)
ax2.set_ylabel('Proposedd numb. of additional resources', fontsize=labelsize)
ax2.set_ylim([-5, 5])
ax1.axhline(y = lowerLimit, color = 'grey')
ax1.axhline(y = upperLimit, color = 'grey')
plt.title('Response time and proposed numb. of additional resources', fontsize=titlesize)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
ps = p1+p2+p3
labels = [label.get_label() for label in ps]
ax1.legend(ps, labels, loc = 2, prop = {'size': 12})
fig.savefig('images/' + filename)
plt.show()
plt.close()
pass
def ScatterPlotsTrainTest(y_train, y_train_predicted, y_test, y_test_predicted, targetVariable):
fig = plt.figure(figsize=(20,7))
fontsize = 10
plt.subplot(1, 2, 1)
plt.title(targetVariable + ' vs its predicted values by Neural Network on train data set', fontsize=fontsize)
plt.scatter(x = y_train, y = y_train_predicted, s = 10)
plt.subplot(1, 2, 2)
plt.title(targetVariable + ' vs its predicted values by Neural Network on test data set', fontsize=fontsize)
plt.scatter(x = y_test, y = y_test_predicted, s = 10)
fig.savefig('images/Y_denormailizedVsY_predictedLatency.jpg')
plt.show()
plt.close()
pass
def ipythonPlotMetricsRealAgainstPredicted(temporaryScalingDF, metricNames):
row = 4; col = 2; j = 1
# fig = plt.figure(figsize=(16,30))
fig = plt.figure(figsize=(20,30))
for i in metricNames:
plt.subplot(row, col, j)
plt.scatter(temporaryScalingDF['next1'+str(i)], temporaryScalingDF['predictedNext1'+str(i)], s = 10)
plt.xlabel('next1'+str(i))
plt.ylabel('predictedNext1'+str(i))
j = j + 1
fig.savefig('images/InnerStateMetricsRegression.png', bbox_inches = 'tight', pad_inches = 0)
plt.show()
plt.close()
pass
def ipythonPlotMetricsRealAgainstPredictedRegression(temporaryScalingDF, metricNames):
row = 9; col = 2; j = 1
fig = plt.figure(figsize=(32,20))
sns.set(style="white", color_codes=True)
for i in metricNames:
# plt.subplot(row, col, j)
# g = sns.jointplot(x=temporaryScalingDF['next1'+str(i)], y=temporaryScalingDF['predictedNext1'+str(i)], kind='reg', height=5, ratio=3)
g = sns.jointplot(x=temporaryScalingDF['next1'+str(i)], y=temporaryScalingDF['predictedNext1'+str(i)], kind='reg', ratio=3)
g.savefig('images/InnerStateMetricRegression' + str(j) + '.jpg')
j = j + 1
plt.show()
# plt.close()
pass