def email(self):
try:
variables = Variables(self.csv_file)
variables.read()
except IOError:
raise SystemExit("%s not found" % self.csv_file)
except csv.Error, e:
raise SystemExit(e.message)
def substitute(self):
from variables import Variables
if self._last_response:
vs = Variables(self._last_response.body)
if self._url:
self.url(vs.substitute(self._url))
if self._header:
self.headers(vs.substitute(self._header))
if self._params:
self.params(vs.substitute(self._params))
if self._data:
self.data(vs.substitute(self._data))
def set_equations(self, conf_equations=None, user=None,
keep_solvers=False, make_virtual=False):
"""
Set equations of the problem using the `equations` problem
description entry.
Fields and Regions have to be already set.
"""
conf_equations = get_default(conf_equations,
self.conf.get_default_attr('equations',
None))
self.set_variables()
variables = Variables.from_conf(self.conf_variables, self.fields)
self.set_materials()
materials = Materials.from_conf(self.conf_materials, self.functions)
self.integrals = self.get_integrals()
equations = Equations.from_conf(conf_equations, variables,
self.domain.regions,
materials, self.integrals,
user=user,
make_virtual=make_virtual)
self.equations = equations
if not keep_solvers:
self.solvers = None
def set_variables(self, conf_variables=None):
conf_variables = get_default(conf_variables, self.conf.variables)
variables = Variables.from_conf(conf_variables, self.fields)
variables.setup_dof_info() # Call after fields.setup_global_base().
self.variables = variables
self.mtx_a = None
self.solvers = None
self.clear_equations()
def create_variables(self, var_names=None):
"""
Create variables with names in `var_names`. Their definitions
have to be present in `self.conf.variables`.
Notes
-----
This method does not change `self.equations`, so it should not
have any side effects.
"""
if var_names is not None:
conf_variables = self.select_variables(var_names, only_conf=True)
else:
conf_variables = self.conf.variables
variables = Variables.from_conf(conf_variables, self.fields)
return variables
import threading
import camera_control
import database_update
import relay_control
import printer_values
from variables import Variables
cam_data = Variables()
camThread = threading.Thread(target=camera_control.camera, args=(cam_data, ))
dbThread = threading.Thread(target=database_update.update, args=(cam_data, ))
relayThread = threading.Thread(target=relay_control.relay, args=(cam_data, ))
printerThread = threading.Thread(target=printer_values.printer_val,
args=(cam_data, ))
camThread.start()
dbThread.start()
relayThread.start()
printerThread.start()
self.scene.addPixmap(pixmap)
self.spinner.stop()
def show_template_manager(self):
"""Launch show_template_manager manager"""
self.template_manager.show()
@pyqtSlot(int)
def update_template(self, an_id: TemplateID):
"""Update Template"""
self.compile(template_id=an_id)
if __name__ == "__main__":
import sys
from db_utils import my_connect
from variables import Variables
app = QApplication(sys.argv)
a_conn = my_connect()
# region Necesary Data for variable details Widget
app_var = Variables(my_conn=a_conn)
a_record = app_var.record(6)
# endregion
dlg = LatexWidget()
dlg.set_latex_data(a_record.latex_obj)
sys.exit(app.exec_())
from variables import Variables
def command_exec(cmd):
"""Executes the command"""
if cmd == '/exit':
print('Bye!')
quit()
elif cmd == '/help':
print('This program calculates all operations. | eg: 1 + 2 * 6 / 5 + (2 - 3) ^ 2')
print('You can also store values in variables. | eg: x = 2')
else:
print('Unknown command')
# Main
calculator = Variables()
print('WELCOME TO THE SMORT CALCULATOR\n\n')
while True:
usr_input = input('Please enter your expression: ')
if usr_input.startswith('/'): # if the user inputs a command
command_exec(usr_input)
else:
if usr_input == '': # if the user didn't input anything
continue
else:
calculator.main(usr_input)
continue
def amdock_load(self):
elements = {
0: 'Working Directory',
1: 'Input Directory',
2: 'Results Directory',
3: 'PDBQT of Target Protein',
4: 'All Poses File of Target Result',
5: 'Best Pose File of Target Result',
6: 'PDBQT of Off-Target Protein',
7: 'All Poses File of Off-Target Result',
8: 'Best Pose File of Off-Target Result'
}
elements_score = {
0: 'Working Directory',
1: 'Input Directory',
2: 'Results Directory',
3: 'PDBQT of Target Protein',
4: 'PDBQT of Ligand'
}
self.AMDock.statusbar.showMessage(" Loading .amdock file...", 2000)
# if self.
self.data = self.AMDock.loader.load_amdock_file()
if self.data:
if self.AMDock.project.mode == 1:
self.table1 = self.data[0]
self.complete = self.data[1]
self.table2 = self.data[2]
else:
self.table1 = self.data[0]
self.complete = self.data[1]
errlist = ''
errlist2 = []
if self.AMDock.project.mode != 2:
for index in range(0, len(self.complete)):
if self.complete[index] == '1':
errlist += '\n-%s' % elements[index]
if len(errlist) != 0:
QtGui.QMessageBox.critical(
self, 'Error',
'Some files defined in .amdock file were not found or '
'they are inaccessible.\nMissing elements:%s' %
errlist)
self.import_text.clear()
Variables.__init__(self.AMDock)
else:
os.chdir(self.AMDock.project.results)
self.prot_label.setText('Target: %s' %
self.AMDock.target.name)
self.prot_label_sel.setText('%s' % self.AMDock.target.name)
self.result_table.setRowCount(len(self.table1))
self.sele1.setRange(1, len(self.table1))
f = 0
for x in self.table1:
c = 0
for item in x:
item = str(item)
self.result_table.setItem(
f, c, QtGui.QTableWidgetItem(item))
self.result_table.item(
f,
c).setTextAlignment(QtCore.Qt.AlignHCenter
| QtCore.Qt.AlignVCenter)
if c == 4:
item_v = float(item)
if item_v <= -0.3:
self.result_table.item(
f, c).setBackgroundColor(
QtGui.QColor(0, 255, 128, 200))
c += 1
f += 1
self.value1 = float(self.result_table.item(0, 1).text())
self.result_table.item(0, 1).setBackgroundColor(
QtGui.QColor('darkGray'))
selection_model = self.result_table.selectionModel()
selection_model.select(
self.result_table.model().index(0, 0),
QtGui.QItemSelectionModel.ClearAndSelect)
if self.AMDock.project.bsd_mode_target == 0:
self.result_table.setHorizontalHeaderLabels(
QtCore.QString(
"Binding Site;Affinity(kcal/mol);Estimated Ki;Ki Units;Ligand Efficiency"
).split(";"))
else:
self.result_table.setHorizontalHeaderLabels(
QtCore.QString(
"Pose;Affinity(kcal/mol);Estimated Ki;Ki Units;Ligand Efficiency"
).split(";"))
if self.AMDock.project.mode == 1:
self.prot_labelB.setText('Off-Target: %s' %
self.AMDock.offtarget.name)
self.prot_label_selB.setText(
'%s' % self.AMDock.offtarget.name)
self.result_tableB.show()
self.selectivity_value_text.show()
self.selectivity.show()
self.sele1.show()
self.sele2.show()
self.prot_label_sel.show()
self.prot_label_selB.show()
self.div.show()
self.equal.show()
self.prot_labelB.show()
self.result_tableB.setRowCount(len(self.table2))
self.sele2.setRange(1, len(self.table2))
f = 0
for x in self.table2:
c = 0
for item in x:
item = str(item)
self.result_tableB.setItem(
f, c, QtGui.QTableWidgetItem(item))
self.result_tableB.item(f, c).setTextAlignment(
QtCore.Qt.AlignHCenter
| QtCore.Qt.AlignVCenter)
if c == 4:
item_v = float(item)
if item_v <= -0.3:
self.result_tableB.item(
f, c).setBackgroundColor(
QtGui.QColor(0, 255, 128, 200))
c += 1
f += 1
self.value2 = float(
self.result_tableB.item(0, 1).text())
self.result_tableB.item(0, 1).setBackgroundColor(
QtGui.QColor('darkGray'))
selection_model = self.result_tableB.selectionModel()
selection_model.select(
self.result_tableB.model().index(0, 0),
QtGui.QItemSelectionModel.ClearAndSelect)
if self.AMDock.project.bsd_mode_offtarget == 0:
self.result_tableB.setHorizontalHeaderLabels(
QtCore.QString(
"Binding Site;Affinity(kcal/mol);Estimated Ki;Ki Units;Ligand Efficiency"
).split(";"))
else:
self.result_tableB.setHorizontalHeaderLabels(
QtCore.QString(
"Pose;Affinity(kcal/mol);Estimated Ki;Ki Units;Ligand Efficiency"
).split(";"))
self.selectivity_value = math.exp(
(self.value2 - self.value1) / (0.001987207 * 298))
self.selectivity_value_text.setText(
'%.01f' % self.selectivity_value)
else:
for index in range(0, len(self.complete)):
if self.complete[index] == '1':
errlist += '\n-%s' % elements_score[index]
errlist2.append(index)
if len(errlist) != 0:
QtGui.QMessageBox.critical(
self, 'Error',
'Some files defined in .amdock file were not found or '
'they are inaccessible.\nMissing elements:%s' %
errlist)
self.import_text.clear()
Variables.__init__(self.AMDock)
else:
os.chdir(self.AMDock.project.results)
self.prot_label.setText('Target: %s' %
self.AMDock.target.name)
self.result_table.setRowCount(len(self.table1))
self.sele1.setRange(1, len(self.table1))
f = 0
for x in self.table1:
c = 0
for item in x:
item = str(item)
self.result_table.setItem(
f, c, QtGui.QTableWidgetItem(item))
self.result_table.item(
f,
c).setTextAlignment(QtCore.Qt.AlignHCenter
| QtCore.Qt.AlignVCenter)
if c == 4:
item_v = float(item)
if item_v <= -0.3:
self.result_table.item(
f, c).setBackgroundColor(
QtGui.QColor(0, 255, 128, 200))
c += 1
f += 1
self.result_table.item(0, 1).setBackgroundColor(
QtGui.QColor('darkGray'))
selection_model = self.result_table.selectionModel()
selection_model.select(
self.result_table.model().index(0, 0),
QtGui.QItemSelectionModel.ClearAndSelect)
# open log if exit
if os.path.exists(
os.path.join(self.AMDock.project.WDIR,
self.AMDock.project.name + '.log')):
self.AMDock.project.log = os.path.join(
self.AMDock.project.WDIR,
self.AMDock.project.name + '.log')
if not self.AMDock.project.log:
return
if os.path.exists(self.AMDock.project.log):
msg = QtGui.QMessageBox.information(
self.AMDock, 'Information', 'This project has a log file.'
' Do you want to open it?',
QtGui.QMessageBox.Yes | QtGui.QMessageBox.No)
if msg == QtGui.QMessageBox.Yes:
self.AMDock.log_widget.textedit.append(
Ft('Opening AMDock Log File...').process())
ofile = open(self.AMDock.project.log)
for line in ofile:
line = line.strip('\n')
self.AMDock.log_widget.textedit.append(line)
ofile.close()
self.AMDock.log_widget.textedit.append(
Ft('Opening AMDock Log File... Done.').process())
try:
self.result_tab.pymol.force_finished()
except:
pass
event.accept()
else:
event.ignore()
from splash_screen import SplashScreen
# from variables import Objects as ob
if __name__ == "__main__":
app = QtGui.QApplication(sys.argv)
app_icon = QtGui.QIcon()
v = Variables()
# dw = QtGui.QDesktopWidget()
app_icon.addFile(v.app_icon, QtCore.QSize(16, 20))
app_icon.addFile(v.app_icon, QtCore.QSize(24, 30))
app_icon.addFile(v.app_icon, QtCore.QSize(32, 40))
app_icon.addFile(v.app_icon, QtCore.QSize(48, 60))
app_icon.addFile(v.app_icon, QtCore.QSize(223, 283))
# app.setStyle("cleanlooks")
app.setWindowIcon(app_icon)
app.setApplicationName(
'AMDock: Assisted Molecular Docking for AutoDock and AutoDock Vina')
splash = SplashScreen(QtGui.QPixmap(v.splashscreen_path), app)
main = AMDock()
splash.finish(main)
main.setWindowState(QtCore.Qt.WindowMaximized)
# main.setMinimumSize(1080, 740)
def add_note(self, note, input_event):
# find insertion index of the note
insert_index = self.index(tkinter.INSERT)
# if the inputted key is a `, then run the break apart event
if note == "`":
self.break_note(insert_index)
return
# check if at end of the score
if int(insert_index.split(".")[1]) == len(self.get(1.0, "end-1c")): # PROBLEM HERE
# if it was a keyboard event, there was an attempt to add text to the text area, remove 2 characters
# if not keyboard, remove one character from the end (just whitespace)
if input_event:
string = self.get(1.0, "end-2c")
else:
string = self.get(1.0, "end-1c")
# convert to upper case for easy keyboard parsing
current_note = note.upper()
# nuke the current text to make sure nothing i dont want added is added
self.delete("1.0", tkinter.END)
self.insert(tkinter.END, string)
# check validity of octave 2 (only high A is allowed)
if Variables.octave == 2 and ord(current_note) > 65:
return
# check if current note fits in the measure
if Variables.current_measure_length + (1.0 / float(Variables.note_length)) > Variables.get_measure_length():
messagebox.showerror("Invalid Configuration", "ERROR: This note is too long to fit in this measure!")
return
# check if the note exists in the naturals table (cross-reference keyboard input to see if valid input)
if current_note in note_lookup.naturals:
# check if rest, then add the rest symbol from the lookup class if so
if current_note == "R":
append = LookupNote.get_rest(Variables.note_length)
# if not a rest, append the note from the symbol generation in the lookup class
else:
append = LookupNote.get_note(current_note, Variables.octave, Variables.note_length,
Variables.accidental, Variables.key_sig)
# insert note
self.insert(tkinter.END, append)
else:
return
# play tone so the user knows what they just added
Playback.play_tone(current_note, Variables.octave, Variables.accidental)
# update the current measure length
Variables.current_measure_length += 1.0 / float(Variables.note_length)
# compare to see if the measure length was just finished, if so add a measure line
if math.isclose(Variables.get_measure_length(), Variables.current_measure_length):
Variables.current_measure_length = 0.0
self.insert(tkinter.END, "!=")
# save the new text so it can be compared again if needed on next input event
self.save_last_text()
else: # note is not at the end of the score
# get the current character at the insertion index
split = insert_index.split(".")
next_index = split[0] + "." + str(int(split[1]) + 1)
character = self.get(insert_index, next_index)
# if the note is one that can be replaced
if LookupNote.replaceable(character):
# parse the insertion index
first_half = self.get(1.0, split[0] + "." + str(int(split[1]) - 1))
second_half = self.get(split[0] + "." + str(int(split[1]) + 2), "end-1c")
# generate new note from the lookup note class using current settings
new_note = LookupNote.get_note(note.upper(), Variables.octave, LookupNote.get_note_length(character),
Variables.accidental, Variables.key_sig)
# if there is an accidental symbol, remove it
if 208 <= ord(first_half[-1]) <= 254:
first_half = first_half[0:len(first_half) - 1]
# delete the old text, insert the new generated text with the new symbol in the insertion index
self.delete(1.0, tkinter.END)
self.insert(tkinter.END, first_half + new_note + second_half)
# play the tone so they know what they inputted
Playback.play_tone(note.upper(), Variables.octave, Variables.accidental)
# save the text for future comparisons
self.save_last_text()
else: # cancel the event if replacement is impossible
self.cancel_event()
def _parse(self, template):
return jinja2.Template(template).render(Variables())
def getCurrentOutsidetemp(self):
"""
Get Current outside temperature from OpenWeatherMap using the API
http://192.168.0.13:3480/data_request?id=status&output_format=xml&DeviceNum=113
http://192.168.0.13:3480/data_request?id=variableget&DeviceNum=112&serviceId=urn:upnp-org:serviceId:TemperatureSensor1&Variable=CurrentTemperature
http://192.168.0.13:3480/data_request?id=variableset&DeviceNum=103&serviceId=urn:upnp-org:serviceId:TemperatureSensor1&Variable=CurrentTemperature&Value=11.4
VeraGetData,http://{}:{}/data_request?id=variableget&DeviceNum={}&serviceId={}&Variable={}
"""
logger = logging.getLogger("main.max.getCurrentOutsidetemp")
Vera_Address, Vera_Port, VeraGetData, VeraOutsideTempID, VeraOutsideTempService, VeraTemp = Variables(
).readVariables([
'VeraIP', 'VeraPort', 'VeraGetData', 'VeraOutsideTempID',
'VeraOutsideTempService', 'VeraTemp'
])
if VeraTemp:
try:
f = urllib2.urlopen(
VeraGetData.format(Vera_Address, Vera_Port,
VeraOutsideTempID,
VeraOutsideTempService,
'CurrentTemperature'))
temp_c = f.read()
t = urllib2.urlopen(
"http://{}:{}/data_request?id=status&output_format=xml&DeviceNum={}"
.format(Vera_Address, Vera_Port, VeraOutsideTempID))
t_xml = t.read()
time_index = int(t_xml.find("LastUpdate")) + 19
update_time = int(t_xml[time_index:(time_index + 10)])
current_time = time.time()
if current_time - update_time <= (15 * 60):
logger.info('Vera Outside Temperature is %s' % temp_c)
return temp_c
else:
logger.info('Vera Outside Temperature out of date')
except Exception, err:
logger.exception("No Vera temp data %s" % err)
def switchHeat(self):
module_logger.info("main.max.switchHeat running")
logTime = time.time()
boilerEnabled, veraControl, Vera_Address, Vera_Port, \
Vera_Device, singleRadThreshold, multiRadThreshold, \
multiRadCount, AllValveTotal, ManualHeatingSwitch, boilerOverride = Variables().readVariables(['BoilerEnabled',
'VeraControl',
'VeraIP',
'VeraPort',
'VeraDevice',
'SingleRadThreshold',
'MultiRadThreshold',
'MultiRadCount',
'AllValveTotal',
'ManualHeatingSwitch',
'BoilerOverride'])
roomTemps = CreateUIPage().createRooms()
outsideTemp = self.getCurrentOutsidetemp()
# Add outside temp to each rooms information
for i in range(len(roomTemps)):
roomTemps[i] = roomTemps[i] + (str(outsideTemp), )
# Calculate if heat is required
valveList = []
for room in roomTemps:
valveList.append(room[4])
singleRadOn = sum(i >= singleRadThreshold for i in valveList)
multiRadOn = sum(i >= multiRadThreshold for i in valveList)
totalRadOn = sum(valveList)
if singleRadOn >= 1 or multiRadOn >= multiRadCount or totalRadOn >= AllValveTotal:
boilerState = 1
else:
boilerState = 0
# Boiler Override
if boilerOverride == 1:
module_logger.info('Boiler overridden ON')
boilerState = 1
if boilerOverride == 0:
boilerState = 0
module_logger.info('Boiler overridden OFF')
module_logger.info("main.max.switchHeat Boiler is %s, Heating is %s" %
(boilerEnabled, boilerState))
# Update Temps database
DbUtils().insertTemps(roomTemps)
if boilerEnabled:
try:
_ = requests.get(veraControl.format(Vera_Address, Vera_Port,
Vera_Device,
str(boilerState)),
timeout=5)
Variables().writeVariable([['VeraOK', 1]])
module_logger.info('message sent to Vera')
except:
Variables().writeVariable([['VeraOK', 0]])
module_logger.info("vera is unreachable")
# Set Manual Boiler Switch if enabled
# if ManualHeatingSwitch:
# module_logger.info("Switching local Relay %s" %boilerState)
# relayHeating(boilerState)
else:
boilerState = 0
try:
_ = requests.get(veraControl.format(Vera_Address, Vera_Port,
Vera_Device, boilerState),
timeout=5)
Variables().writeVariable([['VeraOK', 1]])
module_logger.info("Boiler is Disabled")
except:
Variables().writeVariable([['VeraOK', 0]])
module_logger.info("vera is unreachable")
# Set Manual Boiler Switch if enabled
# if ManualHeatingSwitch:
# module_logger.info("Switching local Relay %s" %boilerState)
# relayHeating(boilerState)
try:
boilerOn = DbUtils().getBoiler()[2]
except:
boilerOn = 9
if boilerState != boilerOn:
msg = (logTime, boilerState)
DbUtils().updateBoiler(msg)
boilerOn = boilerState
# Create UI Pages
CreateUIPage().saveUI(roomTemps)
def maxCmd_M(self, line, refresh):
""" process M response Rooms and Devices"""
logger = logging.getLogger("main.max.maxCmd_M")
expectedRoomNo = int(Variables().readVariables(['ExpectedNoOfRooms']))
line = line.split(",")
es = base64.b64decode(line[2])
room_num = ord(es[2])
logger.info("Number of rooms found : {}".format(room_num))
# Check number of rooms
if room_num != expectedRoomNo:
Variables().writeVariable([['RoomsCorrect', 0]])
logger.info("RoomsCorrect set to : {}".format(0))
else:
Variables().writeVariable([['RoomsCorrect', 1]])
logger.info("RoomsCorrect set to : {}".format(1))
es_pos = 3
this_now = datetime.datetime.now()
for _ in range(0, room_num):
room_id = str(ord(es[es_pos]))
room_len = ord(es[es_pos + 1])
es_pos += 2
room_name = es[es_pos:es_pos + room_len]
es_pos += room_len
room_adr = es[es_pos:es_pos + 3]
es_pos += 3
if room_id not in rooms or refresh:
# id :0room_name, 1room_address, 2is_win_open
rooms.update(
{room_id: [room_name,
self.hexify(room_adr), False]})
dev_num = ord(es[es_pos])
es_pos += 1
for _ in range(0, dev_num):
dev_type = ord(es[es_pos])
es_pos += 1
dev_adr = self.hexify(es[es_pos:es_pos + 3])
es_pos += 3
dev_sn = es[es_pos:es_pos + 10]
es_pos += 10
dev_len = ord(es[es_pos])
es_pos += 1
dev_name = es[es_pos:es_pos + dev_len]
es_pos += dev_len
dev_room = ord(es[es_pos])
es_pos += 1
if dev_adr not in devices or refresh:
# 0type 1serial 2name 3room 4OW,5OW_time, 6status,
# 7info, 8temp offset
devices.update({
dev_adr: [
dev_type, dev_sn, dev_name, dev_room, 0, this_now, 0,
0, 7
]
})
dbMessage = []
for keys in rooms:
roomText = str(rooms[keys][0])
# For testing ' in names
# if roomText == "Bathroom":
# roomText = "Bathroom's"
dbList = keys, roomText, rooms[keys][1]
dbMessage.append(dbList)
print dbMessage
DbUtils().updateRooms(dbMessage)
dbMessage = []
for keys in devices:
dbList = keys, devices[keys][0], devices[keys][1], devices[keys][
2], devices[keys][3], devices[keys][5]
dbMessage.append(dbList)
DbUtils().updateDevices(dbMessage)
class MaxInterface():
# def __init__(self):
# useNeoPixel = Variables().readVariables(['UseNeoPixel'])
# if useNeoPixel:
# self.initialiseNeoPixel()
def initialiseNeoPixel(self):
logger = logging.getLogger("main.max.initialiseNeoPixel")
logger.info("Initialising NeoPixel Serial connection")
self.input_queue = multiprocessing.Queue()
self.output_queue = multiprocessing.Queue()
self.arduino = neopixelserial.SerialProcess(self.input_queue,
self.output_queue)
self.arduino.daemon = True
self.arduino.start()
logger.info(self.arduino)
def checkHeat(self, input_queue):
useNeoPixel = Variables().readVariables(['UseNeoPixel'])
MAXData, validData = self.getData()
if validData:
self.parseData(MAXData)
self.switchHeat()
if useNeoPixel:
self.setNeoPixel(1, input_queue)
def setNeoPixel(self, doChase, input_queue, *args):
light_arg = 0
if args is not None:
for arg in args:
light_arg = arg
logger = logging.getLogger("main.max.setNeoPixel")
logger.info("Setting NeoPixel Lights")
cube_state, vera_state, boiler_enabled, interval, boiler_override, rooms_Ok = Variables(
).readVariables([
'CubeOK', 'VeraOK', 'BoilerEnabled', 'Interval', 'BoilerOverride',
'RoomsCorrect'
])
heating_state = DbUtils().getBoiler()[2]
# overide CubeState if rooms wrong
if not rooms_Ok:
cube_state = 2
sendString = '%s,%s,%s,%s,%s,%s,%s,%s\n\r' % (
doChase, heating_state, interval, boiler_enabled, cube_state,
vera_state, boiler_override, light_arg)
logger.info("Sending NeoPixel %s" % sendString)
input_queue.put(sendString)
def createSocket(self):
logger = logging.getLogger("main.max.createSocket")
# Create Socket
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.settimeout(1)
logger.info('Socket Created')
return s
except socket.error:
logger.exception("unable to create socket")
s.close()
sys.exit()
def getData(self):
logger = logging.getLogger("main.max.getData")
validData = False
message = ""
Max_IP, Max_IP2, Max_Port = Variables().readVariables(
['MaxIP', 'MaxIP2', 'MaxPort'])
logger.info(
'Max Connection Starting on : IP1-%s or IP2-%s on port %s ' %
(Max_IP, Max_IP2, Max_Port))
cube1 = 0
cube2 = 0
success = False
try:
# Connect to Max Cube1
while cube1 < 3 and not success:
try:
logger.info(
'tyring to connect to Max1 on ip %s on try %s' %
(Max_IP, cube1))
s = self.createSocket()
s.connect((Max_IP, int(Max_Port)))
logger.info('Socket Connected to Max1 on ip %s on try %s' %
(Max_IP, cube1))
Variables().writeVariable([['ActiveCube', 1]])
Variables().writeVariable([['CubeOK', 1]])
success = True
except Exception, err:
s.close()
Variables().writeVariable([['CubeOK', 0]])
logger.exception(
"unable to make connection, trying later %s" % err)
time.sleep(2)
cube1 += 1
if not success:
raise Exception("Cube 1 fail")
except Exception, err:
logger.exception(
"unable to make connection to Cube 1, trying Cube2 %s" % err)
# Connect to Max Cube2
while cube2 < 3 and not success:
try:
logger.info(
'tyring to connect to Max2 on ip %s on try %s' %
(Max_IP2, cube2))
s = self.createSocket()
s.connect((Max_IP2, int(Max_Port)))
logger.info('Socket Connected to Max2 on ip %s' % Max_IP2)
Variables().writeVariable([['ActiveCube', 2]])
Variables().writeVariable([['CubeOK', 1]])
success = True
except Exception, err:
s.close()
Variables().writeVariable([['CubeOK', 0]])
logger.exception(
"unable to make connection, trying later %s" % err)
time.sleep(2)
cube2 += 1
Variables().writeVariable([['CubeOK', 0]])
logger.exception(
"unable to make connection, trying later %s" % err)
time.sleep(2)
cube2 += 1
try:
while 1:
reply = s.recv(1024)
message += reply
except:
logger.info("Message ended")
if message != "":
Variables().writeVariable([['CubeOK', 1]])
validData = True
s.close()
logger.debug(message)
return (message, validData)
def parseData(self, message):
print_on = int(Variables().readVariables(['PrintData']))
message = message.split('\r\n')
for lines in message:
# print lines
try:
if lines[0] == 'H':
self.maxCmd_H(lines)
def setUp(self):
self.constraints = Constraints(8)
self.variables = Variables(8)
def __init__(self, embs, data, stage, model_dir = None):
if model_dir is not None:
self._classify = Classify(model_dir)
self._labels = [item.strip() for item in open(model_dir + "/relations.txt").read().splitlines()]
else:
self._labels = None
if stage == "ORACLETEST":
assert(len(data) == 4)
hooks = False
tokens, dependencies, relations, alignments = data
lemmas = None
relations2 = []
self.gold = relations
for r in relations:
if r[1].startswith(":snt"):
r2 = (Node(True),":top",r[2])
else:
r2 = (r[0],r[1],r[2])
if (r2[0].token is not None or r2[1] == ":top") and r2[2].token is not None:
relations2.append(r2)
oracle = Oracle(relations2)
self.variables = Variables()
elif stage == "TRAIN" or stage == "COLLECT":
assert(len(data) == 4)
hooks = False
tokens, dependencies, relations, alignments = data
lemmas = None
relations2 = []
for r in relations:
if r[1].startswith(":snt"):
r2 = (Node(True),":top",r[2])
else:
r2 = (r[0],r[1],r[2])
if (r2[0].token is not None or r2[1] == ":top") and r2[2].token is not None:
relations2.append(r2)
oracle = Oracle(relations2)
self.variables = None
else: #PARSING
assert(len(data) == 2)
hooks = True
tokens, dependencies = data
relations2 = None
alignments = None
oracle = None
self.variables = Variables()
self.state = State(embs, relations2, tokens, dependencies, alignments, oracle, hooks, self.variables, stage, Rules(self._labels))
self.history = History()
while self.state.isTerminal() == False:
#print self.state
tok = copy.deepcopy(self.state.buffer.peek())
if oracle is not None:
action = oracle.valid_actions(self.state)
else:
action = self.classifier()
#print action
#raw_input()
if action is not None:
f_rel = []
f_lab = []
f_reentr = []
if stage == "TRAIN":
f_rel = self.state.rel_features()
if action.name== "larc" or action.name == "rarc":
f_lab = self.state.lab_features()
if action.name == "reduce":
f_reentr = self.state.reentr_features()
self.state.apply(action)
self.history.add((f_rel, f_lab, f_reentr), action, tok)
else:
break
assert (self.state.stack.isEmpty() == True and self.state.buffer.isEmpty() == True)
class Dakota():
def __init__(self, params):
self.workdir = params['general']['workdir']
self.outputfile = params['study']['outputfile']
self.method = get_method(params)
self.njobs = params['study']['njobs']
self.variables = Variables(params)
self.restart = params['study']['restart']
def run(self):
self.generate_input_file()
dakota_command = ['dakota', '-i', 'dakota.in', '-o', 'dakota.log', '-w', 'dakota.rst', '-e', 'dakota.err']
if self.restart:
dakota_command.append(['-r', 'dakota.rst'])
with open(self.workdir + "/SSOPT.log", "w+") as logfile:
with open(self.workdir + "/SSOPT.err", 'w+') as errfile:
sp.Popen(dakota_command, cwd=self.workdir, stderr=errfile, stdout=logfile).wait()
def generate_input_file(self):
self.create_workdir()
with open(self.workdir + '/dakota.in', 'w') as f:
self.print_header(f)
self.print_environment(f)
self.print_model(f)
self.method.print_method(f)
self.print_interface(f)
self.variables.print_variables(f)
self.method.print_responses(f)
def create_workdir(self):
os.mkdir(self.workdir)
@staticmethod
def print_header(inputfile):
inputfile.writelines(file_header)
def print_environment(self, inputfile):
inputfile.writelines(["environment\n",
" tabular_data\n",
" tabular_data_file = '" + self.outputfile + "'\n\n"])
@staticmethod
def print_model(inputfile):
inputfile.writelines("model\n single\n\n")
def print_interface(self, inputfile):
inputfile.writelines(["interface\n",
" fork\n",
" analysis_driver = 'runiteration.py'\n",
" work_directory\n",
" named = './iteration'\n",
" copy_files = '0/' 'constant/' 'system/'\n",
" directory_tag\n",
" directory_save\n",
" failure_capture recover "])
for i in range(0, self.method.number_of_responses):
inputfile.writelines(['NaN '])
inputfile.writelines("\n")
inputfile.writelines(" asynchronous\n evaluation_concurr = ")
inputfile.writelines(str(self.njobs) + "\n\n")
def setNeoPixel(self, doChase, input_queue, *args):
light_arg = 0
if args is not None:
for arg in args:
light_arg = arg
logger = logging.getLogger("main.max.setNeoPixel")
logger.info("Setting NeoPixel Lights")
cube_state, vera_state, boiler_enabled, interval, boiler_override, rooms_Ok = Variables(
).readVariables([
'CubeOK', 'VeraOK', 'BoilerEnabled', 'Interval', 'BoilerOverride',
'RoomsCorrect'
])
heating_state = DbUtils().getBoiler()[2]
# overide CubeState if rooms wrong
if not rooms_Ok:
cube_state = 2
sendString = '%s,%s,%s,%s,%s,%s,%s,%s\n\r' % (
doChase, heating_state, interval, boiler_enabled, cube_state,
vera_state, boiler_override, light_arg)
logger.info("Sending NeoPixel %s" % sendString)
input_queue.put(sendString)
features = data[0]
labels = data[1]
plt.scatter(features[:, 0], features[:, 1], c=labels, cmap='coolwarm')
x = np.linspace(0, 11, 10)
y = -x + 5
plt.plot(x, y)
g = Graph()
graphObject = g.set_as_default()
# Initialize function wx - b | [1,1] * x - 5
x = Placeholder()
graphObject.placeholders.append(x) # append placeholder x
w = Variables([1, 1])
graphObject.variables.append(w) # append variable w
b = Variables(-5)
graphObject.variables.append(b) # append variable b
z = Addition(MatrixMultiplication(w, x, graphObject), b, graphObject)
# Apply activation function
a = Sigmoid(z, graphObject)
# Execute neural network
sess = Session()
print(sess.run(a, {x: [0, -10]}))
plt.show()
def NN(input_list, results_path, seed=123, hidden_layer_sizes=(30,30,30), activation='relu', solver='adam', regularization=0.0001, batch_size='auto', learning_rate_sch='constant', learning_rate_init=0.001, max_iter=100, tol=1e-4, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, n_iter_no_change=10):
"""
1. Train a neural network to classify X based on Y
2. Get classification report
3. Get heatmap of confusion matrix
4. Get ROC curve
Arguments:
- input_list : list, length = 4
Absolute path to [X_train, Y_train, X_test, Y_test]
- results_path: str
Absolute path to the directory where the files will be saved
- seed: int, optional, default = 123
Random seed
- hidden_layer_sizes: tuple, length = n_layers-2, default = (30,30,30)
Number of neurons in each layer
- activation: {'identity', 'logistic', 'tanh', 'relu'}, optional, default = 'relu'
Activation function
- solver: {'lbfgs', 'sgd', 'adam'}, optional, default = 'adam'
Solver
- regularization: float, optional, default = 0.0001
L2 regularization term
- batch_size: int, optional, default = 'auto'
Minibatch size
- learning_rate_sch: {'constant', 'invscaling', 'adaptive'}, optional, default='constant'
Learning rate schedules
- learning_rate_init: float, optional, default = 0.001
Initial learning rate used
- max_iter: int, optional, default = 100
Maximum number of iterations
- tol: float, optional, default = 1e-4
Tolerance for the optimization
- momentum: float, optional, default = 0.9
Momentum for gradient descent update
- nesterovs_momentum: boolean, optional, default = True
Whether to use Nesterov's momentum
- early_stopping: bool, optional, default = False
Whether to use early stopping to terminate training when validation score is not improving
- validation_fraction: float, optional, default = 0.1
Proportion of training data to set aside as validation set for early stopping
- beta_1: float in [0,1), optional, default = 0.9
Exponential decay rate for estimates of first moment vector in adam
- beta_2: float in [0,1), optional, default = 0.999
Exponential decay rate for estimates of second moment vector in adam
- n_iter_no_change: int, optional, default = 10
Maximum number of epochs to not meet tol improvement
Returns:
- Trained neural network
"""
h = Helpers()
v = Variables()
# Diagnostics
h.check_dir(results_path)
if len(input_list) != 4:
logger.error("{0} files found in input_list, expected 4".format(len(input_list)))
h.error()
X_train, Y_train, X_test, Y_test = input_list
h.check_file_existence(X_train)
h.check_file_existence(Y_train)
h.check_file_existence(X_test)
h.check_file_existence(Y_test)
# Import datasets
X_train = h.import_dataset(X_train)
Y_train = h.import_dataset(Y_train)
X_test = h.import_dataset(X_test)
Y_test = h.import_dataset(Y_test)
# Train NN
nn = MLPClassifier(hidden_layer_sizes = hidden_layer_sizes,
activation=activation,
solver=solver,
alpha=regularization,
batch_size=batch_size,
learning_rate=learning_rate_sch,
learning_rate_init=learning_rate_init,
max_iter=max_iter,
random_state=seed,
tol=tol,
momentum=momentum,
nesterovs_momentum=nesterovs_momentum,
early_stopping=early_stopping,
validation_fraction=validation_fraction,
beta_1=beta_1,
beta_2=beta_2,
n_iter_no_change=n_iter_no_change)
nn.fit(X_train, Y_train)
# get accuracy, confusion matrix and ROC AUC
h.get_metrics(nn, [X_train, Y_train, X_test, Y_test], results_path)
return nn
class Phase():
'''A number of rows of text, and a stop condition (string).'''
def __init__(self, label, stop_condition_str, lineno):
self.label = label
self.stop_condition_str = stop_condition_str
self.lineno = lineno
self.lines = list() # List of tuples (line, lineno)
# Set in parse
self.stimulus_elements = None
self.behaviors = None
self.global_variables = None
self.local_variables = None
self.linelabels = list()
self.end_condition = None
self.phase_lines = dict() # Keys are phase line labels, values are PhaseLine objects
self.first_label = None
self.curr_lineobj = None
self.event_counter = None
self.is_first_line = True
self.is_inherited = False
self.is_parsed = False
self.first_stimulus_presented = False
def append_line(self, line, lineno):
self.lines.append((line, lineno))
def parse(self, parameters, global_variables):
self.parameters = parameters
self.global_variables = global_variables
stimulus_elements = parameters.get(STIMULUS_ELEMENTS)
behaviors = parameters.get(BEHAVIORS)
phase_lines_afterlabel = list()
linenos = list()
# First iteration through lines: Create list of lines (and labels)
for line_lineno in self.lines:
line, lineno = line_lineno
label, afterlabel = ParseUtil.split1_strip(line)
if afterlabel is None:
raise ParseException(lineno, "Phase line contains only label.")
coincide_err = f"The phase line label '{label}' coincides with the name of a "
if label in stimulus_elements:
raise ParseException(lineno, coincide_err + "stimulus element.")
elif label in behaviors:
raise ParseException(lineno, coincide_err + "behavior.")
if label in self.linelabels and not self.is_inherited:
raise ParseException(lineno, f"Duplicate of phase line label '{label}'.")
self.linelabels.append(label)
phase_lines_afterlabel.append(afterlabel)
linenos.append(lineno)
if self.first_label is None: # Set self.first_label to the label of the first line
self.first_label = label
# Second iteration: Create PhaseLine objects and put in the dict self.phase_lines
for label, after_label, lineno in zip(self.linelabels, phase_lines_afterlabel, linenos):
self.phase_lines[label] = PhaseLine(self, lineno, label, after_label, self.linelabels,
self.parameters, self.global_variables)
if label == "new_trial": # Change self.first_label to the new_trial line
self.first_label = label
self.initialize_local_variables()
self.event_counter = PhaseEventCounter(self.linelabels, self.parameters)
self.subject_reset()
self.is_parsed = True
def initialize_local_variables(self):
self.local_variables = Variables()
def subject_reset(self):
self.event_counter = PhaseEventCounter(self.linelabels, self.parameters)
self.stop_condition = EndPhaseCondition(self.lineno, self.stop_condition_str)
self._make_current_line(self.first_label)
self.prev_linelabel = None
self.is_first_line = True
self.initialize_local_variables()
self.first_stimulus_presented = False
def next_stimulus(self, response, ignore_response_increment=False, preceeding_help_lines=None, omit_learn=None):
# if not self.is_parsed:
# raise Exception("Internal error: Cannot call Phase.next_stimulus" +
# " before Phase.parse().")
if preceeding_help_lines is None:
preceeding_help_lines = list()
if not ignore_response_increment:
# if not self.is_first_line:
if response is not None:
self.event_counter.increment_count(response)
self.event_counter.increment_count_line(response)
self.event_counter.set_last_response(response)
if self.first_stimulus_presented:
variables_both = Variables.join(self.global_variables, self.local_variables)
if self.stop_condition.is_met(variables_both, self.event_counter):
return None, None, preceeding_help_lines, None
if self.is_first_line:
# assert(response is None)
rowlbl = self.first_label
self.is_first_line = False
omit_learn = True # Since response is None, there is no learning (updating of v-values) to do
else:
rowlbl, omit_learn = self.curr_lineobj.next_line(response, self.global_variables, self.local_variables,
self.event_counter)
self.prev_linelabel = self.curr_lineobj.label
self._make_current_line(rowlbl)
stimulus = self.phase_lines[rowlbl].stimulus
if stimulus is not None:
for element, intensity in stimulus.items():
if type(intensity) is str: # element[var] where var is a (local) variable
variables_both = Variables.join(self.global_variables, self.local_variables)
stimulus[element], err = ParseUtil.evaluate(intensity, variables=variables_both)
if err:
raise ParseException(self.phase_lines[rowlbl].lineno, err)
if rowlbl != self.prev_linelabel:
self.event_counter.reset_count_line()
self.event_counter.line_label = rowlbl
self.event_counter.increment_count(rowlbl)
self.event_counter.increment_count_line(rowlbl)
if stimulus is None: # Help line
action = self.phase_lines[rowlbl].action
lineno = self.phase_lines[rowlbl].lineno
self._perform_action(lineno, action)
preceeding_help_lines.append(rowlbl)
next_stimulus_out = self.next_stimulus(response, ignore_response_increment=True,
preceeding_help_lines=preceeding_help_lines,
omit_learn=omit_learn)
stimulus, rowlbl, preceeding_help_lines, omit_learn_help = next_stimulus_out
omit_learn = (omit_learn or omit_learn_help)
else:
for stimulus_element in stimulus:
self.event_counter.increment_count(stimulus_element)
self.event_counter.increment_count_line(stimulus_element)
self.first_stimulus_presented = True
return stimulus, rowlbl, preceeding_help_lines, omit_learn
def _make_current_line(self, label):
# self.curr_linelabel = label
self.curr_lineobj = self.phase_lines[label]
# self.endphase_obj.update_itemfreq(label)
def perform_actions(self, lineno, actions):
any_omit_learn = False
for action in actions:
omit_learn = self._perform_action(lineno, action)
any_omit_learn = (any_omit_learn or omit_learn)
return any_omit_learn
def _perform_action(self, lineno, action):
"""
Sets a variable (x:3) or count_reset(event).
"""
omit_learn = False
# No action to perform
if len(action) == 0:
pass
# Setting a variable
elif action.count(':') == 1 or action.count('=') == 1:
if action.count('=') == 1:
sep = '='
else:
sep = ':'
var_name, value_str = ParseUtil.split1_strip(action, sep=sep)
variables_join = Variables.join(self.global_variables, self.local_variables)
value, err = ParseUtil.evaluate(value_str, variables_join)
if err:
raise ParseException(lineno, err)
err = self.local_variables.set(var_name, value, self.parameters)
if err:
raise ParseException(lineno, err)
# count_reset
elif action.startswith("count_reset(") and action.endswith(")"):
event = action[12:-1]
self.event_counter.reset_count(event)
# omit_learn
elif action == "@omit_learn":
omit_learn = True
else:
raise ParseException(lineno, "Internal error.") # Should have been caught during Pase.parse()
return omit_learn
def is_phase_label(self, label):
return label in self.linelabels
def check_deprecated_syntax(self):
for _, phase_line in self.phase_lines.items():
msg = phase_line.check_deprecated_syntax()
if msg is not None:
return msg
return None
def copy(self):
cpy = copy.deepcopy(self)
cpy.stop_condition = copy.deepcopy(self.stop_condition)
return cpy
def getVariables(self):
if (self.__variables == None):
self.__variables = Variables(self)
return self.__variables
def LR(input_list, results_path, seed=123, k_folds=10):
"""
1. Perform k-fold logistic regression on X and Y
2. Get heatmap of confusion matrix
3. Get ROC curve
Arguments:
- input_list: list, length = 2 or 4
Absolute path to [X,Y] or [X_train, Y_train, X_test, Y_test]
- results_path: str
Absolute path to the directory where the figures must be saved
- seed: int, optional, default = 123
Random seed
- k_folds: int, optional, default = 10
Number of folds for cross-validation
Returns:
- Trained logistic regression model
"""
h = Helpers()
v = Variables()
# Diagnostics
h.check_dir(results_path)
num_files = len(input_list)
if num_files == 2:
X, Y = input_list
h.check_file_existence(X)
h.check_file_existence(Y)
elif num_files == 4:
X_train, Y_train, X_test, Y_test = input_list
h.check_file_existence(X_train)
h.check_file_existence(Y_train)
h.check_file_existence(X_test)
h.check_file_existence(Y_test)
else:
logger.error(
"{0} files found in input_list, expected 2 or 4".format(num_files))
h.error()
# Import datasets
if num_files == 2:
X = h.import_dataset(X)
Y = h.import_dataset(Y)
else:
X_train = h.import_dataset(X_train)
Y_train = h.import_dataset(Y_train)
X_test = h.import_dataset(X_test)
Y_test = h.import_dataset(Y_test)
# Train LR model
if num_files == 2:
lr = LogisticRegressionCV(solver='liblinear',
cv=k_folds,
random_state=seed)
lr.fit(X, Y)
# get accuracy, classification report, confusion matrix and ROC AUC
h.get_metrics(lr, [X, Y], results_path)
else:
lr = LogisticRegression(solver='liblinear', random_state=seed)
lr.fit(X_train, Y_train)
# get accuracy, classification matrix, confusion matrix and ROC AUC
h.get_metrics(lr, [X_train, Y_train, X_test, Y_test], results_path)
return lr
import time
from database import DbUtils
from variables import Variables
from veratemps import VeraVirtualTemps
import logging
DB = DbUtils()
VAR = Variables()
VT = VeraVirtualTemps()
class CreateUIPage():
def __init__(self):
"""
Create UI Webpage
"""
def updateWebUI(self):
roomTemps = self.createRooms()
self.saveUI(roomTemps)
self.saveAdminUI()
def saveUI(self, roomTemps):
# print 'SAVE UI'
# self.dutyCycle()
pageTop = self.pageTop()
pageHeaderMain = self.pageHeader('Main UI')
pageHeaderAdmin = self.pageHeader('Admin')
buttonLayout = self.buttonLayout()
filler = self.filler()
def get_config(self):
config = {}
config['io_drivers'] = self.io_driver_list.get_config()
config['viewers'] = self.viewers.get_config()
return config
def set_config(self, config):
if 'io_drivers' in config:
self.io_driver_list.set_config(config['io_drivers'])
if 'viewers' in config:
self.viewers.set_config(config['viewers'])
self.variables.clear()
vs = Variables()
viewers = Viewers(variables=vs)
iodl = IODriverList(variables=vs, viewers_instance=viewers)
proj = PlotOMatic(io_driver_list=iodl, variables=vs, viewers=viewers)
proj.start()
proj.configure_traits()
proj.stop()
if PROFILE:
print "Generating Statistics"
yappi.stop()
stats = yappi.get_stats(yappi.SORTTYPE_TSUB, yappi.SORTORDER_DESCENDING,
300) #yappi.SHOW_ALL)
for stat in stats:
class scheduler_test1(unittest.TestCase):
def setUp(self):
self.v = Variables(vmap)
def test1_check_loading(self):
""" Check that the loading f the variables went OK
"""
self.assertEqual(len(self.v.variables), 11)
self.assertEqual(self.v.variables["test"], "False")
self.assertEqual(self.v.variables["housemode"], "At home")
def test2_check_get_variable(self):
""" Check get_variable
"""
val = self.v.get_variable("test")
self.assertEqual(val, "False")
def test3_check_get_variable_negative(self):
""" Check get_variable, negative test case
"""
val = self.v.get_variable("should-not-be-there")
self.assertIsNone(val)
def test4_add_del_update(self):
""" Check adding, changing and deleting a variable
"""
self.assertEqual(self.v.variables["housemode"], "At home")
self.v.set_variable("housemode", "Away")
self.assertEqual(self.v.variables["housemode"], "Away")
val = self.v.get_variable("should-not-be-there")
self.assertRaises(KeyError)
self.v.set_variable("should-not-be-there", "now it's there")
val = self.v.get_variable("should-not-be-there")
self.assertEqual(val, "now it's there")
self.v.del_variable("should-not-be-there")
val = self.v.get_variable("should-not-be-there")
self.assertRaises(KeyError) # OK, it's gone again
def tearDown(self):
pass
def __init__(self):
super(AMDock, self).__init__()
QtGui.QMainWindow.__init__(self)
Variables.__init__(self)
# redirect stderr to log window
sys.stderr = EmittingStream(textWritten=self.normalOutputWritten)
self.checker = Checker(self)
self.output2file = OutputFile(self)
self.loader = Loader(self)
self.project = PROJECT()
self.target = BASE()
self.offtarget = BASE()
self.ligand = BASE()
self.log_thread = QtCore.QThreadPool()
self.numeric_version = [1, 4, 99]
self.version = "{}.{}.{} For Windows and Linux".format(
*self.numeric_version)
self.spacing_autoligand = 1.0
self.spacing_autodock = 0.375
self.pH = 7.40
self.state = 0 # 0 not running, 2 running
self.section = -1 # -1 only PD selected, 0 project, 1 input files, 2 bsd, 3 docking
with open(self.style_file) as f:
self.setStyleSheet(f.read())
self.icon = QtGui.QIcon()
self.icon.addPixmap(QtGui.QPixmap(self.home_icon), QtGui.QIcon.Active,
QtGui.QIcon.Off)
self.icon.addPixmap(QtGui.QPixmap(self.home_icon), QtGui.QIcon.Normal,
QtGui.QIcon.Off)
self.icon.addPixmap(QtGui.QPixmap(self.home_icon),
QtGui.QIcon.Selected, QtGui.QIcon.Off)
self.icon.addPixmap(QtGui.QPixmap(self.home_icon),
QtGui.QIcon.Disabled, QtGui.QIcon.Off)
self.icon.addPixmap(QtGui.QPixmap(self.home_icon_white),
QtGui.QIcon.Selected, QtGui.QIcon.On)
self.icon.addPixmap(QtGui.QPixmap(self.home_icon_white),
QtGui.QIcon.Active, QtGui.QIcon.On)
##--TABS
self.main_window = QtGui.QTabWidget(self)
self.setCentralWidget(self.main_window)
##--Tabs for Docking Options
self.lobby = Lobby(self)
self.main_window.addTab(self.lobby, self.icon, "")
self.program_body = Program_body(self)
self.main_window.addTab(self.program_body, "Docking Options")
self.main_window.setTabEnabled(1, False)
##Tabs for result analysis
self.result_tab = Results(self)
self.main_window.addTab(self.result_tab, "Results Analysis")
self.main_window.setTabEnabled(2, False)
# **Configurations_Tab
self.configuration_tab = Configuration_tab(self)
self.main_window.addTab(self.configuration_tab, "Configuration")
# ** Help_Tab
self.help_tab = Help(self)
self.main_window.addTab(self.help_tab, "Info")
# ** log dockwidget
self.log_widget = LogWindow(self)
self.addDockWidget(QtCore.Qt.RightDockWidgetArea, self.log_widget)
if self.configuration_tab.log_view.isChecked():
self.log_widget.show()
else:
self.log_widget.hide()
self.statusbar = QtGui.QStatusBar(self)
self.statusbar.setObjectName("statusbar")
self.setStatusBar(self.statusbar)
QtCore.QMetaObject.connectSlotsByName(self)
self.version_label = QtGui.QLabel("Version: %s" % self.version)
self.statusbar.addPermanentWidget(self.version_label)
class ScriptParser():
def __init__(self, script):
self.lines = clean_script(script)
self.variables = Variables()
self.parameters = Parameters()
self.phases = Phases()
self.runs = Runs()
self.all_run_labels = list()
self.postcmds = PostCmds()
# Used to label unlabelled @run-statements with "run1", "run2", ...
self.unnamed_run_cnt = 1
# Used to label unlabelled @phase-statements with "phase1", "phase2", ...
self.unnamed_phase_cnt = 1
def parse(self):
if len(self.lines) == 0:
raise ParseException(1, "Script is empty.")
prop = None
curr_phase_label = None
in_prop = False
in_variables = False
in_phase = False
for lineno0, line_orig in enumerate(self.lines):
line = line_orig
lineno = lineno0 + 1
# Handle empty line
if len(line) == 0:
continue
line_endswith_comma = line.endswith(',')
if line_endswith_comma:
line = line[:-1]
line_parser = LineParser(line, self.variables)
line_parser.parse()
linesplit_colon = line_parser.linesplit_colon
linesplit_equal = line_parser.linesplit_equal
linesplit_space = line_parser.linesplit_space
if in_prop or in_variables or in_phase:
parse_this_line_done = True
err = None
if in_prop:
in_prop = line_endswith_comma
err = self.parameters.str_append(prop, line,
self.variables,
self.phases,
self.all_run_labels,
line_endswith_comma)
elif in_variables:
in_variables = line_endswith_comma
err = self.variables.add_cs_varvals(line, self.parameters)
elif in_phase:
in_phase = line_parser.line_type is None
if in_phase:
self.phases.append_line(curr_phase_label, line, lineno)
else:
parse_this_line_done = False
# Phase with label curr_phase_label is complete, parse it
self.phases.parse_phase(curr_phase_label,
self.parameters,
self.variables)
if err:
raise ParseException(lineno, err)
if parse_this_line_done:
continue
if line_parser.line_type == LineParser.PARAMETER:
# Handle line that sets a parameter (e.g. "prop : val")
prop = line_parser.param
if len(linesplit_colon) == 1 and len(linesplit_equal) == 1:
raise ParseException(
lineno, f"Parameter '{prop}' is not specified.")
first_colon_index = line_orig.find(':')
first_equal_index = line_orig.find('=')
if first_equal_index > 0 and first_colon_index > 0:
if first_colon_index < first_equal_index: # u : a=1, b=2
possible_val = linesplit_colon[1].strip()
else: # u = a:1, b:2
possible_val = linesplit_equal[1].strip()
elif first_equal_index > 0 and first_colon_index < 0: # u = 2
possible_val = linesplit_equal[1].strip()
elif first_colon_index > 0 and first_equal_index < 0: # u : 2
possible_val = linesplit_colon[1].strip()
if len(possible_val) == 0:
raise ParseException(
lineno, f"Parameter '{prop}' is not specified.")
if line_endswith_comma:
if not self.parameters.may_end_with_comma(prop):
raise ParseException(
lineno,
"Value for {} may not end by comma.".format(prop))
in_prop = self.parameters.is_csv(prop)
err = self.parameters.str_set(prop, possible_val,
self.variables, self.phases,
self.all_run_labels,
line_endswith_comma)
if err:
raise ParseException(lineno, err)
continue
elif line_parser.line_type == LineParser.VARIABLES:
if len(linesplit_space) == 1:
raise ParseException(lineno, "@VARIABLES not specified.")
in_variables = line_endswith_comma
cs_varvals = linesplit_space[1].strip()
err = self.variables.add_cs_varvals(cs_varvals,
self.parameters)
if err:
raise ParseException(lineno, err)
else:
continue
elif line_parser.line_type == LineParser.PREV_DEFINED_VARIABLE:
err = self.variables.add_cs_varvals(line.strip(),
self.parameters)
if err:
raise ParseException(lineno, err)
else:
continue
elif line_parser.line_type == LineParser.RUN:
if len(linesplit_space) == 1:
raise ParseException(
lineno,
"@RUN line must have the form '@RUN phases [runlabel:label]."
)
after_run = linesplit_space[1].strip()
run_label, run_phase_labels = self._parse_run(
after_run, lineno)
world = self.phases.make_world(run_phase_labels)
run_parameters = copy.deepcopy(
self.parameters) # Params may change betweeen runs
mechanism_obj, err = run_parameters.make_mechanism_obj()
if err:
raise ParseException(lineno, err)
n_subjects = run_parameters.get(kw.N_SUBJECTS)
bind_trials = run_parameters.get(kw.BIND_TRIALS)
is_ok, err, err_lineno = mechanism_obj.check_compatibility_with_world(
world)
if err:
raise ParseException(err_lineno, err)
run = Run(run_label, world, mechanism_obj, n_subjects,
bind_trials)
self.runs.add(run, run_label, lineno)
continue
elif line_parser.line_type == LineParser.PHASE:
gen_err = "@PHASE line must have the form '@PHASE label stop:condition'."
if len(linesplit_space) == 1:
raise ParseException(lineno, gen_err)
lbl_and_stopcond = linesplit_space[1].strip()
curr_phase_label, stop_condition = ParseUtil.split1(
lbl_and_stopcond)
inherited_from = None
if '(' in curr_phase_label and curr_phase_label.endswith(')'):
lind = curr_phase_label.index('(')
inherited_from = curr_phase_label[(lind + 1):-1]
curr_phase_label = curr_phase_label[0:lind]
if not self.phases.contains(inherited_from):
raise ParseException(
lineno, f"Invalid phase label '{inherited_from}'.")
if self.phases.contains(curr_phase_label):
raise ParseException(
lineno, f"Redefinition of phase '{curr_phase_label}'.")
if not curr_phase_label.isidentifier():
raise ParseException(
lineno,
f"Phase label '{curr_phase_label}' is not a valid identifier."
)
if stop_condition is None:
raise ParseException(lineno, gen_err)
stop, condition = ParseUtil.split1_strip(stop_condition, ':')
if stop != "stop" or condition is None or len(condition) == 0:
raise ParseException(
lineno,
"Phase stop condition must have the form 'stop:condition'."
)
in_phase = True
if inherited_from:
self.phases.inherit_from(inherited_from, curr_phase_label,
condition, lineno)
else:
self.phases.add_phase(curr_phase_label, condition, lineno)
continue
elif line_parser.line_type == LineParser.FIGURE:
figure_title, mpl_prop = self._parse_figure(
lineno, linesplit_space)
figure_cmd = FigureCmd(figure_title, mpl_prop)
self.postcmds.add(figure_cmd)
elif line_parser.line_type == LineParser.SUBPLOT:
subplotspec, subplot_title, mpl_prop = self._parse_subplot(
lineno, linesplit_space)
subplot_cmd = SubplotCmd(subplotspec, subplot_title, mpl_prop)
self.postcmds.add(subplot_cmd)
elif line_parser.line_type == LineParser.PLOT:
expr, mpl_prop, expr0 = self._parse_plot(
lineno, linesplit_space)
cmd = linesplit_space[0].lower()
plot_parameters = copy.deepcopy(
self.parameters) # Params may change betweeen plot
self._evalparse(lineno, plot_parameters)
plot_cmd = PlotCmd(cmd, expr, expr0, plot_parameters, mpl_prop)
self.postcmds.add(plot_cmd)
elif line_parser.line_type == LineParser.LEGEND:
mpl_prop = self._parse_legend(lineno, linesplit_space)
legend_cmd = LegendCmd(mpl_prop)
self.postcmds.add(legend_cmd)
elif line_parser.line_type == LineParser.EXPORT:
expr, filename, expr0 = self._parse_export(
lineno, linesplit_space)
cmd = linesplit_space[0].lower()
export_parameters = copy.deepcopy(
self.parameters) # Params may change betweeen exports
self._evalparse(lineno, export_parameters)
export_parameters.val[
kw.
FILENAME] = filename # If filename only given on export line
export_cmd = ExportCmd(lineno, cmd, expr, expr0,
export_parameters)
self.postcmds.add(export_cmd)
else:
raise ParseException(lineno, f"Invalid expression '{line}'.")
def check_deprecated_syntax(self):
return self.phases.check_deprecated_syntax()
def _evalparse(self, lineno, parameters):
"""Handles parameters that depend on currently defined runs."""
if len(self.all_run_labels) == 0:
raise ParseException(lineno, "There is no @RUN.")
run_label = parameters.get(kw.EVAL_RUNLABEL)
if len(run_label) == 0:
run_label = self.all_run_labels[-1]
parameters.val[kw.EVAL_RUNLABEL] = run_label
else:
if run_label not in self.all_run_labels:
raise ParseException(lineno,
f"Unknown run label '{run_label}'.")
def _parse_legend(self, lineno, linesplit_space):
if len(linesplit_space) == 1: # @legend
mpl_prop = dict()
elif len(linesplit_space) == 2: # @legend {mpl_prop}
arg = linesplit_space[1]
is_dict, mpl_prop = ParseUtil.is_dict(arg)
if not is_dict:
raise ParseException(lineno,
f"Invalid @legend argument {arg}.")
return mpl_prop
def _parse_export(self, lineno, linesplit_space):
"""
@export expr
@export expr filename
@hexport
@hexport filename
"""
cmd = linesplit_space[0]
filename_param = self.parameters.get(kw.FILENAME)
if cmd == kw.HEXPORT:
if len(linesplit_space) == 1: # @hexport
if len(filename_param) == 0:
raise ParseException(lineno, f"Invalid {cmd} command.")
else:
filename = filename_param
else: # @hexport filename
filename = linesplit_space[1]
return None, filename, None
if len(linesplit_space) == 1: # @export
raise ParseException(lineno, f"Invalid {cmd} command.")
args = linesplit_space[1]
expr0, filename = ParseUtil.split1_strip(args)
expr = expr0
if filename is None:
if len(filename_param) == 0:
raise ParseException(lineno, f"No filename given to {cmd}.")
else:
filename = filename_param
all_stimulus_elements = self.parameters.get(kw.STIMULUS_ELEMENTS)
all_behaviors = self.parameters.get(kw.BEHAVIORS)
err = None
if cmd == kw.VEXPORT:
expr, err = ParseUtil.parse_element_behavior(
expr0, all_stimulus_elements, all_behaviors)
elif cmd == kw.VSSEXPORT:
expr, err = ParseUtil.parse_element_element(
expr0, all_stimulus_elements)
elif cmd == kw.PEXPORT:
stimulus, behavior, err = ParseUtil.parse_stimulus_behavior(
expr0, all_stimulus_elements, all_behaviors, self.variables)
expr = (stimulus, behavior)
elif cmd == kw.NEXPORT:
expr, err = ParseUtil.parse_chain(expr0, all_stimulus_elements,
all_behaviors)
if err:
raise ParseException(lineno, err)
return expr, filename, expr0
def _parse_plot(self, lineno, linesplit_space):
"""
@plot expr {mpl_prop}
"""
cmd = linesplit_space[0]
if len(linesplit_space) == 1: # @plot
raise ParseException(lineno, f"Invalid {cmd} command.")
expr0, mpl_prop = ParseUtil.get_ending_dict(linesplit_space[1])
if mpl_prop is None:
mpl_prop = dict()
expr = expr0
all_stimulus_elements = self.parameters.get(kw.STIMULUS_ELEMENTS)
all_behaviors = self.parameters.get(kw.BEHAVIORS)
err = None
if cmd == kw.VPLOT:
expr, err = ParseUtil.parse_element_behavior(
expr0, all_stimulus_elements, all_behaviors)
elif cmd == kw.VSSPLOT:
expr, err = ParseUtil.parse_element_element(
expr0, all_stimulus_elements)
elif cmd == kw.PPLOT:
stimulus, behavior, err = ParseUtil.parse_stimulus_behavior(
expr0, all_stimulus_elements, all_behaviors, self.variables)
expr = (stimulus, behavior)
elif cmd == kw.NPLOT:
expr, err = ParseUtil.parse_chain(expr0, all_stimulus_elements,
all_behaviors)
if err:
raise ParseException(lineno, err)
return expr, mpl_prop, expr0
def _parse_subplot(self, lineno, linesplit_space):
"""
@subplot
@subplot subplotspec
@subplot subplotspec title
@subplot subplotspec {mpl_prop}
@subplot subplotspec title {mpl_prop}
"""
title_param = self.parameters.get(kw.SUBPLOTTITLE)
if len(linesplit_space) == 1: # @subplot
subplotspec = '111'
title = title_param
mpl_prop = dict()
elif len(linesplit_space) == 2: # @subplot ...
args, mpl_prop = ParseUtil.get_ending_dict(linesplit_space[1])
if mpl_prop is None:
mpl_prop = dict()
subplotspec, title_line = ParseUtil.split1_strip(args)
if title_line is None: # @subplot subplotspec
title = title_param
else:
title = title_line
return subplotspec, title, mpl_prop
def _parse_figure(self, lineno, linesplit_space):
"""
@figure
@figure title
@figure {mpl_prop}
@figure title {mpl_prop}
"""
title = self.parameters.get(kw.TITLE)
if len(linesplit_space) == 1: # @figure
mpl_prop = dict()
elif len(linesplit_space) == 2: # @figure args
title, mpl_prop = ParseUtil.get_ending_dict(linesplit_space[1])
if mpl_prop is None:
mpl_prop = dict()
return title, mpl_prop
def _parse_run(self, after_run, lineno):
"""
Parses a @RUN line ("@RUN phase1,phase2,... [runlabel:lbl]") and returns the run label and
a list of phase labels.
"""
match_objs_iterator = re.finditer(r' runlabel[\s]*:', after_run)
match_objs = tuple(match_objs_iterator)
n_matches = len(match_objs)
if n_matches == 0:
label = f'run{self.unnamed_run_cnt}'
self.unnamed_run_cnt += 1
phases_str = after_run
elif n_matches == 1:
match_obj = match_objs[0]
start_index = match_obj.start() + 1 # Index of "l" in "label"
end_index = match_obj.end() # Index of character after ":"
label = after_run[end_index:].strip()
phases_str = after_run[0:start_index].strip()
else:
raise ParseException(
lineno,
f"Maximum one instance of 'runlabel:' on a {kw.RUN} line.")
if label in self.all_run_labels:
raise ParseException(lineno,
f"Duplication of run label '{label}'.")
else:
self.all_run_labels.append(label)
phase_labels = phases_str.strip(',').split(',')
phase_labels = [phase_label.strip() for phase_label in phase_labels]
for phase_label in phase_labels:
if not self.phases.contains(phase_label):
raise ParseException(lineno, f"Phase {phase_label} undefined.")
return label, phase_labels
def next_stimulus(self, response, ignore_response_increment=False, preceeding_help_lines=None, omit_learn=None):
# if not self.is_parsed:
# raise Exception("Internal error: Cannot call Phase.next_stimulus" +
# " before Phase.parse().")
if preceeding_help_lines is None:
preceeding_help_lines = list()
if not ignore_response_increment:
# if not self.is_first_line:
if response is not None:
self.event_counter.increment_count(response)
self.event_counter.increment_count_line(response)
self.event_counter.set_last_response(response)
if self.first_stimulus_presented:
variables_both = Variables.join(self.global_variables, self.local_variables)
if self.stop_condition.is_met(variables_both, self.event_counter):
return None, None, preceeding_help_lines, None
if self.is_first_line:
# assert(response is None)
rowlbl = self.first_label
self.is_first_line = False
omit_learn = True # Since response is None, there is no learning (updating of v-values) to do
else:
rowlbl, omit_learn = self.curr_lineobj.next_line(response, self.global_variables, self.local_variables,
self.event_counter)
self.prev_linelabel = self.curr_lineobj.label
self._make_current_line(rowlbl)
stimulus = self.phase_lines[rowlbl].stimulus
if stimulus is not None:
for element, intensity in stimulus.items():
if type(intensity) is str: # element[var] where var is a (local) variable
variables_both = Variables.join(self.global_variables, self.local_variables)
stimulus[element], err = ParseUtil.evaluate(intensity, variables=variables_both)
if err:
raise ParseException(self.phase_lines[rowlbl].lineno, err)
if rowlbl != self.prev_linelabel:
self.event_counter.reset_count_line()
self.event_counter.line_label = rowlbl
self.event_counter.increment_count(rowlbl)
self.event_counter.increment_count_line(rowlbl)
if stimulus is None: # Help line
action = self.phase_lines[rowlbl].action
lineno = self.phase_lines[rowlbl].lineno
self._perform_action(lineno, action)
preceeding_help_lines.append(rowlbl)
next_stimulus_out = self.next_stimulus(response, ignore_response_increment=True,
preceeding_help_lines=preceeding_help_lines,
omit_learn=omit_learn)
stimulus, rowlbl, preceeding_help_lines, omit_learn_help = next_stimulus_out
omit_learn = (omit_learn or omit_learn_help)
else:
for stimulus_element in stimulus:
self.event_counter.increment_count(stimulus_element)
self.event_counter.increment_count_line(stimulus_element)
self.first_stimulus_presented = True
return stimulus, rowlbl, preceeding_help_lines, omit_learn
def apply(self, action):
if action.name == "shift":
#pred = self.nextSubgraph()
token = self.buffer.consume()
sg = action.argv.get()
#pred2 = pred.get_str(None, Variables())
#sg2 = sg.get_str(None, Variables())
#if sg2 == pred2:
# print "MATCH"
#else:
# print "MISMATCH"
if self.stage == "COLLECT":
Resources.phrasetable[token.word + "_" +
token.pos][action.argv.get(
None, Variables())] += 1
if token.ne in ["ORGANIZATION", "ORG"
] and token.word not in Resources.seen_org:
Resources.seen_org.append(token.word)
Resources.forg.write(token.word)
for node in sg.nodes:
if node.isConst == False and node.concept.strip(
) != "":
Resources.forg.write(" " + node.concept)
Resources.forg.write("\n")
test = []
for n in sg.nodes:
if len([r for r in sg.relations
if r[1] == n]) == 0: # push only root
self.stack.push(n)
test.append(n)
break
tmprels = Relations()
for n1, n2, label in sg.relations:
self.stack.relations.add(n1, n2, label)
tmprels.add(n1, n2, label)
self.counter += 1
if len(sg.nodes) == 0:
graph = "NULL"
elif tmprels == Relations():
graph = "(" + sg.nodes[0].concept + ")"
else:
graph, _, _ = tostring.to_string(tmprels.triples(), "TOP")
#print self.sentence, "|||", self.counter - 1, "|||", " ".join(graph.replace("\n","").split())
elif action.name == "reduce":
node = self.stack.pop()
if action.argv is not None:
s, label, _ = action.argv
self.stack.relations.add(node, s, label)
elif action.name == "larc":
label = action.argv
child = self.stack.get(1)
top = self.stack.top()
assert (top is not None and child is not None)
self.stack.relations.add(top, child, label)
self.stack.pop(1)
elif action.name == "rarc":
label = action.argv
child = self.stack.get(1)
top = self.stack.top()
assert (top is not None and child is not None)
self.stack.relations.add(child, top, label)
else:
raise ValueError("action not defined")
def setUp(self):
self.v = Variables(vmap)
class Helpers:
"""
Private functions
"""
v = Variables()
def error(self):
"""
Print onto the console a message if an error is encountered during execution
Arguments:
- No arguments
Returns:
- No return values
"""
print("Execution terminated. Check {0} for details".format(
self.v.log_f))
sys.exit(1)
def create_file(self, filename):
"""
Create a file if it does not exist
Arguments:
- filename: str
Absolute path of the file that has to be created
Returns:
- No return values
"""
try:
os.path.exists(filename)
except IOError:
logger.info("Attempting to create {0}".format(filename))
try:
f = open(filename, 'w')
f.close()
except IOError:
logger.error("Unable to create {0}".format(filename))
self.error()
logger.info("{0} successfully created".format(filename))
return None
def done(self):
"""
Print onto the console a message that execution has completed
Arguments:
- No arguments
Returns:
- No return values
"""
print("Execution completed")
return None
def check_file_existence(self, filename):
"""
Check if a file exists and throw error if not
Arguments:
- filename: str
Absolute path of the file whose existence is to be determined
Returns:
- No return values
"""
try:
assert (os.path.exists(filename))
except AssertionError:
logger.error("{0} does not exist".format(filename))
self.error()
return None
def check_year(self, start, end):
"""
Check if end year is greater than the start year
Arguments:
- start: int
Starting year
- end: int
Ending year
Returns:
- No return values
"""
self.check_integer(start)
self.check_integer(end)
try:
assert (end > start)
except AssertionError:
logger.error("End year must be greater than start year")
self.error()
return None
def check_extension(self, filename, ext):
"""
Validates the extension of the file
Arguments:
- filename: str
Name of the file
- ext: str
Extension of the file
Returns:
- No return values
"""
try:
current = filename.split('.')[-1]
assert (current == ext)
except AssertionError:
logger.error("Expected extension {0}, got {1}".format(
ext, current))
self.error()
def import_dataset(self, abs_path):
"""
Imports the dataset
Arguments:
- abs_path: str
Absolute path to the dataset file
Returns:
- data: pandas dataframe
Dataset as a pandas dataframe
"""
self.check_file_existence(abs_path)
logger.info("Reading {0}".format(abs_path))
ext = abs_path.split('.')[-1]
if ext == 'csv':
data = pd.read_csv(abs_path)
elif ext == 'xls':
data = pd.read_excel(abs_path)
else:
logger.error("Expected extensions csv or xls, got {0}".format(ext))
self.error()
logger.info("{0} has {1} rows and {2} columns".format(
abs_path, data.shape[0], data.shape[1]))
return data
def write_dataset(self, dataset, abs_path):
"""
Writes dataset to a file
Arguments:
- dataset: pandas dataframe
Dataset to be written
- abs_path: str
Absolute path to the file where the dataset must be written
Returns:
- No return value
"""
logger.info("Creating {0}".format(abs_path))
ext = abs_path.split('.')[-1]
if ext == 'csv':
dataset.to_csv(abs_path, index=False)
elif ext == 'xls':
dataset.to_excel(abs_path, index=False)
else:
logger.error("Expected extensions csv or xls, got {0}".format(ext))
self.error()
logger.info("{0} successfully generated".format(abs_path))
return None
def read_feature_file(self, filename):
"""
read the features in filename
Arguments:
- filename: str
Absolute path to the file where the features are newline separated strings
Returns:
- features: list
List of features
"""
with open(filename, 'r') as f:
features = f.readlines()
features = [d.split('\n')[0] for d in features]
return features
def isfloat(self, inp):
"""
Check if input is a floating-point number or not
Arguments:
- inp: Input
Returns:
True if floating-point number, False otherwise
"""
try:
float(inp)
return True
except ValueError:
return False
def check_integer(self, number):
"""
Ensure that input is a number
Arguments:
- number: Input
Returns:
- No return values
"""
try:
assert (str(number).isnumeric())
except AssertionError:
logger.error("Expected type {0}, got type {1} for {2}".format(
type(number), 'int', number))
self.error()
def check_float(self, number):
"""
Ensure that input is a floating-point number
Arguments:
- number: Input
Returns:
- No return values
"""
try:
assert (self.isfloat(number))
except AssertionError:
logger.info("Expected type {0}, got type {1} for {2}".format(
type(number), 'float', number))
self.error()
def confmat_heatmap(self, cm, score, path):
"""
Generate the heatmap for the confusion matrix
Arguments:
- cm: array
confusion matrix
- score: float
Accuracy
- path: str
Absolute path to the directory where the heatmap will be generated
Returns:
- No return values
"""
plt.figure(figsize=(9, 9))
sns.heatmap(cm,
annot=True,
fmt=".3f",
linewidths=.5,
square=True,
cmap='Blues_r')
plt.ylabel('True label')
plt.xlabel('Predicted label')
all_sample_title = 'Accuracy Score: {0}'.format(score)
plt.title(all_sample_title, size=15)
plt.savefig(path)
logger.info("{0} successfully generated".format(path))
return None
def roc_auc(self, fpr, tpr, auc, path):
"""
Generate the ROC AUC curve
Arguments:
- fpr: float
False positive rates
- tpr: float
True positive rates
- auc: float
ROC AUC score
- path: str
Absolute path to the directory where the curve must be saved
Returns:
- No return values
"""
plt.figure(figsize=(9, 9))
plt.plot(fpr, tpr, color='red', lw=2, label='ROC curve')
plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC curve with AUC: {0}'.format(auc), size=15)
plt.savefig(path)
logger.info("{0} successfully generated".format(path))
return None
def check_dir(self, path):
"""
Check if directory is present and create it if not present
Arguments:
- path: str
Absolute path to the directory
Returns:
- No return values
"""
if not os.path.exists(path):
try:
logger.info("Attempting to create {0} directory".format(path))
os.makedirs(path)
logger.info("{0} directory successfully created".format(path))
except OSError:
logger.error("Unable to create {0} directory".format(path))
self.error()
def get_metrics(self,
model,
data_list,
results_path,
accuracy=True,
class_report=True,
confmat=True,
roc_auc=True):
"""
Compute the accuracy, classification report, confusion matrix and ROC AUC curve
Arguments:
- model: Model whose metrics must be computed
- data_list: list, length = 2 or 4
Dataset [X, Y]
or
Training and testing datasets [X_train, Y_train, X_test, Y_test]
- results_path: str
Absolute path to the directory where the metric files must be saved
- accuracy: bool, optional, default = True
Whether or not to log accuracy
- class_report: bool, optional, default = True
Whether or not to generate class_report_train.txt and class_report_test.txt
- confmat: bool, optional, default = True
Whether or not to generate confmat_train.png and confmat_test.png
- roc_auc: bool, optional, default = True:
Whether or not to generate roc_auc_train.png and roc_auc_test.png
Returns:
- No return values
"""
if len(data_list) == 2:
X, Y = data_list
score = model.score(X, Y)
pred = model.predict(X)
if accuracy:
logger.info("accuracy = {0}".format(score))
if class_report:
cr = classification_report(Y, pred)
cr_file = os.path.join(results_path, 'class_report.txt')
self.create_file(cr_file)
with open(cr_file, 'w') as f:
f.write(cr)
logger.info(
"Classification report generated: {0}".format(cr_file))
if confmat:
cm = confusion_matrix(Y, pred)
self.confmat_heatmap(cm, score,
os.path.join(results_path, 'confmat.png'))
if roc_auc:
fpr, tpr, _ = roc_curve(Y, model.predict_proba(X)[:, 1])
auc_score = auc(fpr, tpr)
logger.info("ROC AUC = {0}".format(auc_score))
self.roc_auc(fpr, tpr, auc_score,
os.path.join(results_path, 'roc_auc.png'))
elif len(data_list) == 4:
X_train, Y_train, X_test, Y_test = data_list
train_score = model.score(X_train, Y_train)
test_score = model.score(X_test, Y_test)
train_pred = model.predict(X_train)
test_pred = model.predict(X_test)
if accuracy:
logger.info("Training accuracy = {0}".format(train_score))
logger.info("Testing accuracy = {0}".format(test_score))
if class_report:
train_cr = classification_report(Y_train, train_pred)
train_cr_file = os.path.join(results_path,
'class_report_train.txt')
self.create_file(train_cr_file)
with open(train_cr_file, 'w') as f:
f.write(train_cr)
logger.info(
"Training classification report generated: {0}".format(
train_cr_file))
test_cr = classification_report(Y_test, test_pred)
test_cr_file = os.path.join(results_path,
'class_report_test.txt')
self.create_file(test_cr_file)
with open(test_cr_file, 'w') as f:
f.write(test_cr)
logger.info(
"Testing classification report generated: {0}".format(
test_cr_file))
if confmat:
train_cm = confusion_matrix(Y_train, train_pred)
self.confmat_heatmap(
train_cm, train_score,
os.path.join(results_path, 'confmat_train.png'))
test_cm = confusion_matrix(Y_test, test_pred)
self.confmat_heatmap(
test_cm, test_score,
os.path.join(results_path, 'confmat_test.png'))
if roc_auc:
train_fpr, train_tpr, _ = roc_curve(
Y_train,
model.predict_proba(X_train)[:, 1])
train_auc = auc(train_fpr, train_tpr)
logger.info("Training ROC AUC = {0}".format(train_auc))
self.roc_auc(train_fpr, train_tpr, train_auc,
os.path.join(results_path, 'roc_auc_train.png'))
test_fpr, test_tpr, _ = roc_curve(
Y_test,
model.predict_proba(X_test)[:, 1])
test_auc = auc(test_fpr, test_tpr)
logger.info("Testing ROC AUC = {0}".format(test_auc))
self.roc_auc(test_fpr, test_tpr, test_auc,
os.path.join(results_path, 'roc_auc_test.png'))
else:
logger.error("expected list of length 2 or 4, got {0}".format(
len(data_list)))
self.error()
return None
def decision_tree_viz(self, model, path):
"""
Generate visualization of decision tree
Arguments:
- model: Decision tree model
- path: str
Absolute path to the file where the decision tree visualization is to be generated
Returns:
- No return values
"""
dot_data = StringIO()
export_graphviz(model,
out_file=dot_data,
filled=True,
rounded=True,
special_characters=True,
feature_names=self.read_feature_file(
self.v.features_f),
class_names=['0', '1'])
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
self.check_extension(path, 'png')
graph.write_png(path)
logger.info("{0} successfully generated".format(path))
return None
def get_one_roc_curve(self, data_list, results_path, logit, dec_tree,
neural_net):
"""
Generate all ROC curves in one plot
Arguments:
- data_list: list, length = 2
Test set [X_test,Y_test]
- results_path: str
Absolute path to the directory where the ROC curve must be generated
- data_list: list, length = 4
Training and testing datasets [X_train, Y_train, X_test, Y_test]
- logit: Logistic Regression model
- dec_tree: Decision tree model
- neural_net: Neural network model
Returns:
- No return value
"""
if len(data_list) != 2:
logger.error("Expected array of length 2, got {0}".format(
len(data_list)))
self.error()
plt.figure(figsize=(9, 9))
X, Y = data_list
self.check_file_existence(X)
self.check_file_existence(Y)
X = self.import_dataset(X)
Y = self.import_dataset(Y)
if logit != None:
logit_fpr, logit_tpr, _ = roc_curve(Y,
logit.predict_proba(X)[:, 1])
logit_auc_score = auc(logit_fpr, logit_tpr)
plt.plot(
logit_fpr,
logit_tpr,
color='red',
lw=2,
label='Logistic regression, AUC = {0}'.format(logit_auc_score))
if dec_tree != None:
dec_tree_fpr, dec_tree_tpr, _ = roc_curve(
Y,
dec_tree.predict_proba(X)[:, 1])
dec_tree_auc_score = auc(dec_tree_fpr, dec_tree_tpr)
plt.plot(
dec_tree_fpr,
dec_tree_tpr,
color='blue',
lw=2,
label='Decision tree, AUC = {0}'.format(dec_tree_auc_score))
if neural_net != None:
neural_net_fpr, neural_net_tpr, _ = roc_curve(
Y,
neural_net.predict_proba(X)[:, 1])
neural_net_auc_score = auc(neural_net_fpr, neural_net_tpr)
plt.plot(
neural_net_fpr,
neural_net_tpr,
color='green',
lw=2,
label='Neural network, AUC = {0}'.format(neural_net_auc_score))
plt.plot([0, 1], [0, 1], color='yellow', lw=2, linestyle='--')
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC curves'.format(auc), size=15)
plt.legend(loc='upper left')
reqd_file = os.path.join(results_path, "final_roc.png")
plt.savefig(reqd_file)
logger.info("{0} successfully generated".format(reqd_file))
return None
def split(dataset, target, test_size, output_direc, seed=123):
"""
1. Import dataset
2. Take only individuals where target is not NaN in dataset
3. X: Data for required features
4. Y: Data for target
5. Split X and Y into train and test
6. X_unknown and Y_unknown where Y is missing
7. Save X, Y, X_train, Y_train, X_test, Y_test, X_unknown, Y_unknown to output_direc
Arguments:
- dataset: str
Absolute path to the preprocessed dataset which has to be split
- target: str
The target feature
- test_size: float
Size of the test set (percentage)
- output_direc: str
Directory where training and test directory must be placed
- seed: int, optional, default = 123
Random seed value
Returns:
- No return value
"""
h = Helpers()
v = Variables()
# Diagnostics
h.check_file_existence(dataset)
h.check_float(test_size)
h.check_file_existence(output_direc)
h.check_integer(seed)
h.check_dir(output_direc)
# Import dataset
data = h.import_dataset(dataset)
# Take only those individuals where target is not NaN
data_nonNaN = data[data[target].notnull()]
logger.info(
"{0} individuals do not have missing values in their target attribute".
format(data_nonNaN.shape[0]))
data_NaN = data[data[target].isnull()]
# Read features from features file
features = h.read_feature_file(v.features_f)
# Get X and Y
X = data_nonNaN[features]
Y = data_nonNaN[target]
# Split X and Y into training and testing data
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=test_size,
random_state=seed)
# Feature scaling
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
# Get X_unknown and Y_unknown
X_unknown = data_NaN[features]
Y_unknown = data_NaN[target]
# Convert these np arrays to pd dataframes
X = pd.DataFrame(X, columns=features)
Y = pd.DataFrame(Y, columns=[target])
X_train = pd.DataFrame(X_train, columns=features)
Y_train = pd.DataFrame(Y_train, columns=[target])
X_test = pd.DataFrame(X_test, columns=features)
Y_test = pd.DataFrame(Y_test, columns=[target])
X_unknown = pd.DataFrame(X_unknown, columns=features)
Y_unknown = pd.DataFrame(Y_unknown, columns=[target])
# Save train, test and unknown to output_direc
h.write_dataset(X, os.path.join(output_direc, 'X.csv'))
h.write_dataset(Y, os.path.join(output_direc, 'Y.csv'))
h.write_dataset(X_train, os.path.join(output_direc, 'X_train.csv'))
h.write_dataset(Y_train, os.path.join(output_direc, 'Y_train.csv'))
h.write_dataset(X_test, os.path.join(output_direc, 'X_test.csv'))
h.write_dataset(Y_test, os.path.join(output_direc, 'Y_test.csv'))
h.write_dataset(X_unknown, os.path.join(output_direc, 'X_unknown.csv'))
h.write_dataset(Y_unknown, os.path.join(output_direc, 'Y_unknown.csv'))
return None
def initialize_local_variables(self):
self.local_variables = Variables()