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_cost(model, model_df, cost_func, save_pdf=False):
output = '/tmp/wikipedia_cost.pdf' if save_pdf else None
df = _select_df(model_df, 'application', 'wikipedia')
target = _train(model, df)
plotter = Plotter(xlim=(0, 3.5))
plotter.update_config(s=40)
plotter.plot_cost_model(model, target, cost_func, output)
print('Prediction of the Wikipedia application cost.')
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.')
def predict_example_2(fd, recogniter):
"""
データはランダムに選択された関数から取得してnetwrokに入力.
関数を確率を表示する.
"""
plotter_2 = Plotter()
result = defaultdict(list)
plotter_2.initialize({
'xy_value':{
'ylim': [0,100],
'sub_title': ['value']},
'likelihood':{
'ylim': [0,1],
'sub_title': fd.function_list.keys()},
}, movable=True)
#for ftype in fd.function_list.keys():
print '################"'
for idx in range(100):
ftype = fd.romdom_choice()
data = fd.get_data(ftype)
for x, y in data:
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': None
}
inferences = recogniter.run(input_data, learn=False)
# print
input_data['ftype'] = ftype
recogniter.print_inferences(input_data, inferences)
tmp = inferences[ "classifier_" + recogniter.selectivity]['likelihoodsDict']
plotter_2.write_draw(title='xy_value', x_value={'value': x + 100 * idx}, y_value={'value': y})
plotter_2.write_draw(title='likelihood', x_value=dict([(sub, x + 100 * idx) for sub in tmp.keys()]), y_value=tmp)
from lib.msplot import *
import easygui
from lib.data import Data, AuxPlots
from lib.plotter import Plotter
import numpy as np
if __name__ == '__main__':
path = easygui.diropenbox()
d = Data()
d.load(path)
p = Plotter(d)
print(d.aux_data)
# if __name__ == '__main__':
# path = easygui.diropenbox()
# s = Spectrum()
# s.load(path, onblast_cutoff=150)
# #s.load("C:\\Users\\ishma\\Dropbox (SteinLab)\\spectra\\(ID 186nm) 2016-07-26-tip15")
# #s.load("../(ID 186nm) 2016-07-26-tip15/")
p.makeAnimation(window=500,
step=20,
scan_range=(None, None),
mass_range=(None, None),
out_name="test.mp4",
normalization=SpecNorm.SCAN,
label_peaks=True,
def main():
type_a_data = [2] * 10 + [50] * 30
type_b_data = [1] * 10 + [50] * 30
data_set = {'a': type_a_data, 'b': type_b_data}
recogniter = FunctionRecogniter()
plotter = Plotter()
result = defaultdict(list)
plotter.initialize(
{
'anomaly': {
'ylim': [0, 1],
'sub_title': ['a', 'b']
},
'output-differ': {
'ylim': [0, 1],
'sub_title': ['a', 'b']
},
'first': {
'ylim': [0, 100],
'sub_title': ['a', 'b']
},
},
movable=True)
# トレーニング
for i in range(100):
anomaly_mean = {}
output_differ_mean = {}
first_input = {}
for ftype, data in data_set.items():
tmp_anomaly = []
tmp_output_differ = []
first_input_cnt = 0
for x, y in enumerate(data):
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': ftype
}
inferences = recogniter.run(input_data, learn=True)
# for plot
tmp_anomaly.append(inferences['anomaly']['region1'])
if len(inferences["output_differ"]) == 0:
first_input_cnt += 1
else:
tmp_output_differ.append(
inferences["output_differ"]['region1'])
# print
#recogniter.print_inferences(input_data, inferences)
recogniter.reset()
# for plot
anomaly_mean[ftype] = sum(tmp_anomaly) / len(tmp_anomaly)
output_differ_mean[ftype] = sum(tmp_output_differ) / len(
tmp_output_differ)
first_input[ftype] = first_input_cnt
plotter.write_draw(title='anomaly',
x_value={
'a': i,
'b': i
},
y_value=anomaly_mean)
plotter.write_draw(title='output-differ',
x_value={
'a': i,
'b': i
},
y_value=output_differ_mean)
plotter.write_draw(title='first',
x_value={
'a': i,
'b': i
},
y_value=first_input)
# 予測
#if i % 10 == 0:
print
print '##################### ', i
predict_example_4(data_set, recogniter)
plotter.show()
def __init__(self):
QMainWindow.__init__(self)
self.__config = ConfigObj("./config/seasnake.conf")
self.setMinimumSize(
QSize(int(self.__config['general']['win_width']),
int(self.__config['general']['win_height'])))
self.dateutil = DateUtility()
#print self.dateutil.todate(self.dateutil.currentepoch()), self.dateutil.currentepoch()
self.plugins = []
self.plot = Plotter()
self.plot.setMinimumHeight(600)
self.linelist = LineFeather(self)
self.todaymark = InfiniteLine(angle=90,
movable=False,
pos=(self.dateutil.currentepoch()),
name="Now",
pen="g")
self.todaymark.setFocus()
self.plot._plt.addItem(self.todaymark, ignoreBounds=True)
self.plot.region.setRegion([
int(self.dateutil.currentepoch() - 7200),
int(self.dateutil.currentepoch() + 36000)
])
self.timer = QTimer()
self.timer.timeout.connect(self.updateUI)
self.timer.setInterval(10)
self.timer.start(50)
self.status = StatusBar()
self.setStatusBar(self.status)
self.statuslayout = QHBoxLayout()
self.__maindisplay = QGroupBox()
self.mainlayout = QFormLayout()
self.mainlayout.expandingDirections()
self.__maindisplay.setLayout(self.mainlayout)
self.mainlayout.addRow(self.linelist, self.plot)
self.mainlayout.addRow(self.linelist.chkplot,
self.linelist.chkfpredict)
self.datapanel = QFormLayout()
self.dslayout = QVBoxLayout()
self.importerlayout = QVBoxLayout()
self.importerlayout.setAlignment(Qt.AlignTop)
self.importerdock = self.__build_dock
self.importerdock.setLayout(self.importerlayout)
self.progressbar = QProgressBar()
self.progressbar.setMaximumWidth(100)
self.progressbar.setMaximum(100)
self.status.addPermanentWidget(self.progressbar)
self.btntrigger = QPushButton("Start Prediction")
self.btntrigger.clicked.connect(self.btntrigger_click)
self.constituent_opts = QComboBox()
self.datasource = DataSourceController(self)
for k, v in self.__config['constituent'].items():
self.constituent_opts.addItem(k)
self.constcheck[k] = QCheckBox(k)
self.constcheck[k].toggled.connect(self.btntrigger_click)
self.dock = self.__dock
self.dock.setWindowTitle("Options")
self.tab = QTabWidget()
self.tab.addTab(self.importerdock, "Importer")
self.tab.addTab(self.__build_dspanel, "Datasource")
self.tab.addTab(self.__build_dock, "Utilities")
self.tab.addTab(self.__build_dock, "About")
self.tab.setCurrentIndex(1)
self.dock.setWidget(self.tab)
self.addDockWidget(Qt.LeftDockWidgetArea, self.dock)
self.setCentralWidget(self.__maindisplay)
class MainWindow(QMainWindow):
constcheck = {}
def __init__(self):
QMainWindow.__init__(self)
self.__config = ConfigObj("./config/seasnake.conf")
self.setMinimumSize(
QSize(int(self.__config['general']['win_width']),
int(self.__config['general']['win_height'])))
self.dateutil = DateUtility()
#print self.dateutil.todate(self.dateutil.currentepoch()), self.dateutil.currentepoch()
self.plugins = []
self.plot = Plotter()
self.plot.setMinimumHeight(600)
self.linelist = LineFeather(self)
self.todaymark = InfiniteLine(angle=90,
movable=False,
pos=(self.dateutil.currentepoch()),
name="Now",
pen="g")
self.todaymark.setFocus()
self.plot._plt.addItem(self.todaymark, ignoreBounds=True)
self.plot.region.setRegion([
int(self.dateutil.currentepoch() - 7200),
int(self.dateutil.currentepoch() + 36000)
])
self.timer = QTimer()
self.timer.timeout.connect(self.updateUI)
self.timer.setInterval(10)
self.timer.start(50)
self.status = StatusBar()
self.setStatusBar(self.status)
self.statuslayout = QHBoxLayout()
self.__maindisplay = QGroupBox()
self.mainlayout = QFormLayout()
self.mainlayout.expandingDirections()
self.__maindisplay.setLayout(self.mainlayout)
self.mainlayout.addRow(self.linelist, self.plot)
self.mainlayout.addRow(self.linelist.chkplot,
self.linelist.chkfpredict)
self.datapanel = QFormLayout()
self.dslayout = QVBoxLayout()
self.importerlayout = QVBoxLayout()
self.importerlayout.setAlignment(Qt.AlignTop)
self.importerdock = self.__build_dock
self.importerdock.setLayout(self.importerlayout)
self.progressbar = QProgressBar()
self.progressbar.setMaximumWidth(100)
self.progressbar.setMaximum(100)
self.status.addPermanentWidget(self.progressbar)
self.btntrigger = QPushButton("Start Prediction")
self.btntrigger.clicked.connect(self.btntrigger_click)
self.constituent_opts = QComboBox()
self.datasource = DataSourceController(self)
for k, v in self.__config['constituent'].items():
self.constituent_opts.addItem(k)
self.constcheck[k] = QCheckBox(k)
self.constcheck[k].toggled.connect(self.btntrigger_click)
self.dock = self.__dock
self.dock.setWindowTitle("Options")
self.tab = QTabWidget()
self.tab.addTab(self.importerdock, "Importer")
self.tab.addTab(self.__build_dspanel, "Datasource")
self.tab.addTab(self.__build_dock, "Utilities")
self.tab.addTab(self.__build_dock, "About")
self.tab.setCurrentIndex(1)
self.dock.setWidget(self.tab)
self.addDockWidget(Qt.LeftDockWidgetArea, self.dock)
self.setCentralWidget(self.__maindisplay)
def updateUI(self):
self.todaymark.setPos(self.dateutil.currentepoch())
#print self.todaymark.getPos()
@property
def xaxis(self):
ax = DateAxis(orientation='bottom')
return ax
def setPlugins(self, pluginarray=[]):
self.plugins = pluginarray
def btntrigger_click(self):
self.datasource.selected.constituent = {}
self.datasource.clearplot()
for k, v in self.constcheck.items():
if self.constcheck[k].isChecked():
self.datasource.selected.constituent[k] = self.__config[
'constituent'][str(v.text())]
if len(self.datasource.selected.constituent.keys()) != 0:
self.datasource.selected.bootup()
@property
def __build_dspanel(self):
gb = QGroupBox()
gb.setAlignment(Qt.AlignCenter)
gblayout = QVBoxLayout()
gblayout.addWidget(self.datasource.dscheckbox)
for i in self.constcheck.values():
gblayout.addWidget(i)
gblayout.addWidget(self.datasource.paramgb)
gblayout.addWidget(self.btntrigger)
gb.setLayout(gblayout)
return gb
@property
def __build_dock(self):
gb = QGroupBox()
return gb
@property
def __dock(self):
dock = QDockWidget(self)
dock.setMinimumWidth(300)
dock.setMinimumHeight(100)
dock.setAllowedAreas(Qt.LeftDockWidgetArea | Qt.RightDockWidgetArea)
return dock
@property
def menubar(self):
return self.menuBar()
@property
def toolbar(self):
return self.toolButtonStyle()
def main():
type_a_data = [2] * 10 + [50] * 30
type_b_data = [1] * 10 + [50] * 30
data_set = {'a': type_a_data, 'b':type_b_data}
recogniter = FunctionRecogniter()
plotter = Plotter()
result = defaultdict(list)
plotter.initialize({
'anomaly':{
'ylim': [0,1],
'sub_title': ['a', 'b']},
'output-differ':{
'ylim': [0,1],
'sub_title': ['a', 'b']},
'first':{
'ylim': [0,100],
'sub_title': ['a', 'b']},
}, movable=True)
# トレーニング
for i in range(100):
anomaly_mean = {}
output_differ_mean = {}
first_input = {}
for ftype, data in data_set.items():
tmp_anomaly = []
tmp_output_differ = []
first_input_cnt = 0
for x, y in enumerate(data):
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': ftype
}
inferences = recogniter.run(input_data, learn=True)
# for plot
tmp_anomaly.append(inferences['anomaly']['region1'] )
if len(inferences["output_differ"]) == 0:
first_input_cnt += 1
else:
tmp_output_differ.append(inferences["output_differ"]['region1'])
# print
#recogniter.print_inferences(input_data, inferences)
recogniter.reset()
# for plot
anomaly_mean[ftype] = sum(tmp_anomaly) / len(tmp_anomaly)
output_differ_mean[ftype] = sum(tmp_output_differ) / len(tmp_output_differ)
first_input[ftype] = first_input_cnt
plotter.write_draw(title='anomaly', x_value={'a':i, 'b':i}, y_value=anomaly_mean)
plotter.write_draw(title='output-differ', x_value={'a':i, 'b':i}, y_value=output_differ_mean)
plotter.write_draw(title='first', x_value={'a':i, 'b':i}, y_value=first_input)
# 予測
#if i % 10 == 0:
print
print '##################### ', i
predict_example_4(data_set, recogniter)
plotter.show()
def predict_example_3(fd, recogniter):
"""
各層の統計的特徴を比較する.
1. 各層のclassifier結果のgraph表示.
2.
"""
plotter = Plotter()
result = defaultdict(lambda: defaultdict(list))
plotter.initialize({
# 'selectivity_center':{
# 'ylim': [0,100],
# 'sub_title': recogniter.dest_resgion_data.keys() },
# 'selectivity_outside':{
# 'ylim': [0,100],
# 'sub_title': recogniter.dest_resgion_data.keys() },
'xy_value':{
'ylim': [0,100],
'sub_title': ['value']},
'likelihood':{
'ylim': [0,1],
'sub_title': recogniter.dest_resgion_data.keys() },
}, movable=False)
for ftype in fd.function_list.keys():
print ftype
data = fd.get_data(ftype)
for x, y in data:
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': None
}
inferences = recogniter.run(input_data, learn=False)
# print
input_data['ftype'] = ftype
recogniter.print_inferences(input_data, inferences)
# for result summary
for name in recogniter.dest_resgion_data.keys():
tmp = inferences[ "classifier_" + name ]['likelihoodsDict'][ftype]
result[name][ftype].append(tmp)
# for plot
plotter.write(title="xy_value", x_value={'value': x}, y_value={'value': y})
tmp = {}
for name in recogniter.dest_resgion_data.keys():
class_name = "classifier_" + name
tmp[name] = inferences[class_name]['likelihoodsDict'][ftype]
plotter.write(title="likelihood", y_value=tmp)
# # for plot
# x_tmp = {}
# y_tmp = {}
# for name in recogniter.dest_resgion_data.keys():
# x_tmp[name] = recogniter.evaluation[name].get_selectivity()[ftype]['x']
# y_tmp[name] = recogniter.evaluation[name].get_selectivity()[ftype]['y']
# plotter.add(title="selectivity_center", x_values=x_tmp, y_values=y_tmp)
#
# x_tmp2 = {}
# y_tmp2 = {}
# for name in recogniter.dest_resgion_data.keys():
# x_tmp2[name] = recogniter.evaluation_2[name].get_selectivity()[ftype]['x']
# y_tmp2[name] = recogniter.evaluation_2[name].get_selectivity()[ftype]['y']
# plotter.add(title="selectivity_outside", x_values=x_tmp2, y_values=y_tmp2)
plotter.show(save_dir='./docs/images/multi_layer/', file_name='each-layer-'+ftype+'.png')
plotter.reset()
# write result summary
import numpy
print '### result'
for name, datas in result.items():
print '#### ', name
for title ,data in datas.items():
print title , " : ",
print numpy.mean(data)
# print evaluation summary
for name in recogniter.dest_resgion_data.keys():
print '### ', name
recogniter.evaluation[name].print_summary()
def predict_example(fd, recogniter):
"""
全ての関数から得たデータをnetwrokに入力, 関数を予測する.
最終的に, 関数毎の平均正解率と入力に対する正解率を表示する.
neuronの選択性が得られているかを表示.
"""
plotter = Plotter()
result = defaultdict(list)
plotter.initialize({
'xy_value':{
'ylim': [0,100],
'sub_title': ['value']},
'likelihood':{
'ylim': [0,1],
'sub_title': fd.function_list.keys()},
}, movable=False)
for ftype in fd.function_list.keys():
print ftype
data = fd.get_data(ftype)
for x, y in data:
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': None
}
inferences = recogniter.run(input_data, learn=False)
# print
input_data['ftype'] = ftype
recogniter.print_inferences(input_data, inferences)
# for result summary
tmp = inferences[ "classifier_" + recogniter.selectivity]['likelihoodsDict'][ftype]
result[ftype].append(tmp)
# for plot
plotter.write(title="xy_value", x_value={'value': x}, y_value={'value': y})
plotter.write(title="likelihood", y_value=inferences[ "classifier_" + recogniter.selectivity]['likelihoodsDict'])
plotter.show(save_dir='./docs/images/multi_layer/', file_name='2layer-'+ftype+'.png')
plotter.reset()
# write result summary
import numpy
print '### result'
for title , data in result.items():
print title , " : ",
print numpy.mean(data)
# print evaluation summary
for name in recogniter.dest_resgion_data.keys():
print '### ', name
recogniter.evaluation[name].print_summary()