def plot_wikipedia(predictor, save_pdf=False):
"""Plot wikipedia target prediction and real values."""
target, model = predictor.get_plot_data('wikipedia')
output = '/tmp/wikipedia.pdf' if save_pdf else None
plotter = Plotter(xlim=(3, 65), ylim=(0, 200))
plotter.plot_model(model, target, output)
print('Prediction of the Wikipedia application target execution duration.')
def plot_wikipedia(model, model_df, save_pdf=False):
"""Plot actual results and model's predictions."""
output = '/tmp/wikipedia.pdf' if save_pdf else None
df = _select_df(model_df, 'application', 'wikipedia')
target = _train(model, df)
plotter = Plotter(xlim=(3, 65), ylim=(0, 200))
plotter.plot_model(model, target, output)
print('Prediction of the Wikipedia application target execution duration.')
def plot_hbsort(predictor, save_pdf=False):
"""Plot actual results and model's predictions."""
target, model = predictor.get_plot_data('hbsort')
outputs = _get_outputs(save_pdf, 'hbsort3.pdf', 'hbsort30.pdf')
plotter = Plotter(xlim=(0.75, 16.25))
plotter.plot_model(model, target[target.input < 15 * 1024**3], outputs[0])
plotter = Plotter(xlim=(14, 130))
plotter.plot_model(model, target[target.input > 15 * 1024**3], outputs[1])
print('The top figure is the result of the HiBench Sort application with'
' 3-GB input. The second figure uses 31-GB of data.')
def plot_hbkmeans(predictor, save_pdf=False):
"""Plot actual results and model's predictions."""
target, model = predictor.get_plot_data('hbkmeans')
outputs = _get_outputs(save_pdf, 'hbkmeans16.pdf', 'hbkmeans65.pdf')
plotter = Plotter(xlim=(7.5, 32.5))
plotter.plot_model(model, target[target.input == 16384000], outputs[0])
plotter = Plotter(xlim=(30, 130))
plotter.plot_model(model, target[target.input == 65536000], outputs[1])
print('The top figure is the result of the HiBench K-means application'
' with 16,384,000 samples. The second figure uses 65,536,000'
' samples.')
