def create_new_adaboost_classifiers(po, algs):
"""
:param algs: The algorithms which will be the classifiers of one of the adaboosts
:param po: The object which holds the tweets organized and parsed
:return: Returns a list containing the two adaboost that are created in this function
"""
adas = [
ClassifiersWrapper(
Adaboost.Adaboost(algs), Converters.TweetToTweet(),
"Adaboost with all the algorithms from before as classifiers")
]
ic_list = []
for i in range(900):
ic = ClassifiersWrapper(
IndexClassifier.IndexClassifier(my_index=i),
Converters.TweetToTweet())
ic.train(po.get_tweets_before_for_others(),
po.get_tweets_after_for_others())
ic_list.append(ic)
adas.append(
ClassifiersWrapper(
Adaboost.Adaboost(ic_list),
Converters.TweetToBowWithPca(new_dim=900),
"Adaboost with every index as a classfier (after PCA to 900)"))
return adas
def __init__(self, filename, select_y = 'INCMIN', select_x = ['DOSAGE', 'SEX', 'RACE_1']):
self.base_file = '../data_reg/' + os.path.basename(filename).split('.')[0]
self.select_x = select_x
self.select_y = select_y
self.train_file, self.val_file = Converters.split_orangetab_into_2(filename, randomize=True)
self.X, self.Y = self.__read_into_array(self.train_file, remove_constants=True)
self.Xv, self.Yv = self.__read_into_array(self.val_file, remove_constants=False)
def __init__(self, decl_list, tid, tag):
assert isinstance(decl_list, (list, tuple, deque,))
assert len(decl_list) > 0
assert isinstance(tid, int)
assert isinstance(tag, str)
self.decl_list = tuple(d.replace("> >", ">>") for d in decl_list)
self.decl_list_no_const = [d for d in decl_list if d != "const"]
self.tid = tid
self.tag = tag
self.cvt = Converters.find(self)
def create_new_other_classifiers():
"""
Creates an object for a classifier with the class it uses to convert the raw data to a vector
:return: Returns a list of wrappers which wraps the conversion object and the algorithm object
"""
return [
ClassifiersWrapper(MLPClassifier(hidden_layer_sizes=(500, 500)),
Converters.TweetToBow(), "MLP with BOW"),
ClassifiersWrapper(NearestCentroid(), Converters.TweetToBow(),
"NearestCentroid with BOW"),
ClassifiersWrapper(linear_model.LogisticRegression(),
Converters.TweetToBow(), "Log Reg with BOW"),
ClassifiersWrapper(svm.LinearSVC(), Converters.TweetToBow(),
"SVM with BOW"),
ClassifiersWrapper(MLPClassifier(hidden_layer_sizes=(500, 500)),
Converters.TweetToLetvec(), "MLP with LetVec"),
ClassifiersWrapper(NearestCentroid(), Converters.TweetToLetvec(),
"NearestCentroid with LetVec"),
ClassifiersWrapper(linear_model.LogisticRegression(),
Converters.TweetToLetvec(),
"Log Reg with LetVec"),
ClassifiersWrapper(svm.LinearSVC(), Converters.TweetToLetvec(),
"SVM with LetVec")
]
# Modules is separating code into multiple files import Converters # Do not need to add the extensions... aka .py int necessary from Converters import kg_to_lbs # you can import specific functions print(Converters.kg_to_lbs(70)) kg_to_lbs(100)
def _test():
import CodeBlock
import Converters
import HeaderJar
header_jar = HeaderJar.HeaderJar()
Session.begin(header_jar)
Converters.add(Converters.WcsConv())
tk = TupleAndKeywords()
tk.add_parameter(
Argument.Argument(Types.Type((
"int",
"*",
), 1, "FundamentalType"), "foo"))
tk.add_parameter(
Argument.Argument(Types.Type((
"double",
"&",
), 2, "FundamentalType"), "bar"))
tk.add_parameter(
Argument.Argument(
Types.Type((
"long unsigned int",
"&",
"const",
), 3, "FundamentalType"), "xyz"))
tk.add_parameter(
Argument.Argument(Types.Type((
"X",
"const",
"&",
), 4, "Class"), "x"))
tk.add_parameter(
Argument.Argument(Types.Type((
"Y",
"*",
), 5, "Class"), "y"))
tk.add_parameter(Argument.Argument(Types.Type(("Z", ), 6, "Class"), "z"))
tk.add_parameter(
Argument.Argument(Types.Type(("bool", ), 7, "FundamentalType"), "b"))
tk.add_parameter(
Argument.Argument(
Types.Type((
"wchar_t",
"const",
"*",
), 8, "PointerType"), "str"))
print(tk.get_fmt_specifier())
print(tk.get_keywords())
print(tk.build_function_signature_parameter_list())
from Module import PythonNamer
namer = PythonNamer()
print(tk.build_parser_idecl(namer=namer))
_print_empty_line()
block = CodeBlock.CodeBlock()
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
_print_empty_line()
_print_empty_line()
tk = TupleAndKeywords()
tk.add_parameter(
Argument.Argument(Types.Type((
"int",
"*",
), 1, "FundamentalType"), "foo", "nullptr"))
tk.add_parameter(
Argument.Argument(Types.Type((
"double",
"&",
), 2, "FundamentalType"), "bar", "PI"))
tk.add_parameter(
Argument.Argument(
Types.Type((
"long unsigned int",
"&",
"const",
), 3, "FundamentalType"), "xyz", "MAXINT"))
tk.add_parameter(
Argument.Argument(Types.Type((
"X",
"const",
"&",
), 4, "Class"), "x", "_x"))
tk.add_parameter(
Argument.Argument(Types.Type((
"Y",
"*",
), 5, "Class"), "y", "_py"))
tk.add_parameter(
Argument.Argument(Types.Type(("Z", ), 6, "Class"), "z", "Z(1990)"))
tk.add_parameter(
Argument.Argument(Types.Type(("bool", ), 7, "FundamentalType"), "b",
"true"))
tk.add_parameter(
Argument.Argument(
Types.Type((
"wchar_t",
"const",
"*",
), 8, "PointerType"), "str", 'L"Hello world!"'))
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
integer = Types.Type(("int", ), 99, "FundamentalType")
Converters.add(Converters.ListConv(integer))
tk = TupleAndKeywords()
tk.add_parameter(
Argument.Argument(
Types.Type((
"std::vector<int>",
"const",
"&",
), 0, "Class"), "vi", "_vi"))
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
K = Types.Type((
"wchar_t",
"const",
"*",
), 111, "PointerType")
V = Types.Type((
"wxColour",
"*",
), 112, "PointerType")
Converters.add(Converters.DictConv(K, V))
tk = TupleAndKeywords()
tk.add_parameter(
Argument.Argument(
Types.Type((
"std::map<wchar_t const *, wxColour *>",
"&",
), 0, "Class"), "m"))
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
def fullExtractMatlab(filename):
global BIAS_RESISTORS
global ST_COEFF
fs = None
if (os.path.isfile(filename + '.params')):
with open(filename + '.params') as temp:
value = temp.readline()
fs = int(''.join(list(filter(str.isdigit, value))))
print("Autoelecting fs: " + str(fs))
elif (os.path.isfile(filename + '.npz')):
npzfile = np.load(filename + 'npz')
BIAS_RESISTORS = npzfile['BIAS_RESISTORS']
ST_COEFF = npzfile['ST_COEFF']
fs = npzfile['fs']
print("Autoelecting fs: " + str(fs))
print("Loaded Calibration Data")
else:
raise Exception("Missing Calibration Data (.params or .npz file)")
print("Extracting: " + filename)
data = None
with open(filename, 'rb') as f:
data = np.fromfile(f, dtype=np.uint16)
ref = data[0::4]
probe = data[1::4]
vap = data[2::4]
voltage = data[3::4]
QVC = Converters.QVConverter()
ref = QVC.convert(ref)
probe = QVC.convert(probe)
vap = QVC.convert(vap)
voltage = QVC.convert(voltage)
limit = np.std(voltage)
badpts1 = np.where(voltage > np.mean(voltage) + limit)[0]
badpts2 = np.where(voltage < np.mean(voltage) - limit)[0]
badpts = np.concatenate((badpts1, badpts2))
meanRef = np.mean(ref)
meanProbe = np.mean(probe)
meanVap = np.mean(vap)
meanVoltage = np.mean(voltage)
ref[badpts] = meanRef
probe[badpts] = meanProbe
vap[badpts] = meanVap
voltage[badpts] = meanVoltage
VRref = Converters.VRConverter(BIAS_RESISTORS[0])
VRprobe = Converters.VRConverter(BIAS_RESISTORS[1])
RTref = Converters.RTConverter(ST_COEFF[0])
RTprobe = Converters.RTConverter(ST_COEFF[1])
ref = RTref.convert(VRref.convert(ref, voltage))
probe = RTprobe.convert(VRprobe.convert(probe, voltage))
print("Creating Matlab File: " + filename + '.mat')
outData = {}
outData['BIAS_RESISTORS'] = BIAS_RESISTORS
outData['ST_COEFF'] = ST_COEFF
outData['fs'] = fs
outData['reference'] = ref
outData['probe'] = probe
outData['vap'] = vap
outData['voltage'] = voltage
matWriter.savemat(filename, outData)
print("Done")
import Converters from Converters import kg_to_lbs print(kg_to_lbs(70)) print(Converters.lbs_to_kg(154)) # Without "Converters" instance this will throw error # since we did not do specific function import
def save_data(input_data, Bay_Assignment, kpi_coeffs, solve_status):
print('Exporting data to Excel: ...')
start_time_export = time.time()
# Converting input_data from list to dataframe (for later)
input_dataframe = CONV.inputs_list2dataframe(input_data)
output_dataframe = pd.DataFrame(input_dataframe)
# Exporting inputs to a csv file (for eventual later use)
input_dataframe.to_csv('./outputs/Generated Inputs.csv')
if solve_status.lower() == 'optimal':
# Adding assigned bay to flights
bay_assignment = list(np.zeros(len(input_data)))
passenger_coeffs = list(np.zeros(len(input_data)))
preference_coeffs = list(np.zeros(len(input_data)))
towing_coeffs = list(np.zeros(len(input_data)))
for decision_variable in Bay_Assignment.iter_binary_vars():
if int(decision_variable.solution_value) == 1:
dv, var_type, flight_index, bay = decision_variable.name.split(
'_')
if var_type == 'x':
passenger_coeffs[int(flight_index)] = -kpi_coeffs[0][
'_'.join([var_type, flight_index, bay])]
preference_coeffs[int(flight_index)] = kpi_coeffs[1][
'_'.join([var_type, flight_index, bay])]
bay_assignment[int(flight_index)] = bay
if var_type == 'v':
towing_coeffs[int(flight_index)] = -kpi_coeffs[2]['_'.join(
[var_type, flight_index, bay])]
if (decision_variable.name.find('v') != -1
or decision_variable.name.find('w') != -1
or decision_variable.name.find('u') != -1) and int(
decision_variable.solution_value):
#print(decision_variable.name, decision_variable.solution_value)
pass
output_dataframe['Bay'] = bay_assignment
output_dataframe['Passenger Coefficients'] = passenger_coeffs
output_dataframe['Preference Coefficients'] = preference_coeffs
output_dataframe['Towing Coefficients'] = towing_coeffs
# Checking for towings:
long_stays = output_dataframe[output_dataframe['long stay']]
arrivals = long_stays[long_stays['move type'] == 'Arr'].reset_index()
parkings = long_stays[long_stays['move type'] == 'Park'].reset_index()
departures = long_stays[long_stays['move type'] == 'Dep'].reset_index()
towings_ = []
for i in range(len(list(arrivals['Bay']))):
towing_arr_park = 'NO'
towing_park_dep = 'NO'
if arrivals['Bay'].iloc[i] != parkings['Bay'].iloc[i]:
towing_arr_park = 'YES'
if parkings['Bay'].iloc[i] != departures['Bay'].iloc[i]:
towing_park_dep = 'YES'
towings_.append({
'Fl No. Arrival':
arrivals['Fl No. Arrival'].iloc[i],
'Arrival Bay':
arrivals['Bay'].iloc[i],
'Park Bay':
parkings['Bay'].iloc[i],
'Departure Bay':
departures['Bay'].iloc[i],
'Arr -> Park':
towing_arr_park,
'Park -> Dep':
towing_park_dep
})
#print (towings)
towings_dataframe = pd.DataFrame.from_records(towings_)
if len(towings_dataframe) > 0:
towings_dataframe = towings_dataframe[[
'Fl No. Arrival', 'Arrival Bay', 'Park Bay', 'Departure Bay',
'Arr -> Park', 'Park -> Dep'
]]
else:
towings_dataframe['Fl No. Arrival'] = [0]
towings_dataframe['Arrival Bay'] = [0]
towings_dataframe['Park Bay'] = [0]
towings_dataframe['Departure Bay'] = [0]
towings_dataframe['Arr -> Park'] = [0]
towings_dataframe['Park -> Dep'] = [0]
# Exporting all data to excel
export_2_excel(input_dataframe, output_dataframe, towings_dataframe)
print('Exporting data to Excel: DONE (' +
str(round(time.time() - start_time_export, 3)) + ' seconds)\n')
# Creating Charts
print('Generating charts: ...')
start_time_charts = time.time()
ChC.generate_charts(input_dataframe, output_dataframe,
towings_dataframe, solve_status)
print('Generating charts: DONE (' +
str(round(time.time() - start_time_charts, 3)) + ' seconds)\n')
return output_dataframe
def _test():
import CodeBlock
import Converters
import HeaderJar
header_jar = HeaderJar.HeaderJar()
Session.begin(header_jar)
Converters.add(Converters.WcsConv())
tk = TupleAndKeywords()
tk.add_parameter(Argument.Argument(Types.Type(("int", "*",), 1, "FundamentalType"), "foo"))
tk.add_parameter(Argument.Argument(Types.Type(("double", "&",), 2, "FundamentalType"), "bar"))
tk.add_parameter(Argument.Argument(Types.Type(("long unsigned int", "&", "const",), 3, "FundamentalType"), "xyz"))
tk.add_parameter(Argument.Argument(Types.Type(("X", "const", "&",), 4, "Class"), "x"))
tk.add_parameter(Argument.Argument(Types.Type(("Y", "*",), 5, "Class"), "y"))
tk.add_parameter(Argument.Argument(Types.Type(("Z",), 6, "Class"), "z"))
tk.add_parameter(Argument.Argument(Types.Type(("bool",), 7, "FundamentalType"), "b"))
tk.add_parameter(Argument.Argument(Types.Type(("wchar_t", "const", "*",), 8, "PointerType"), "str"))
print(tk.get_fmt_specifier())
print(tk.get_keywords())
print(tk.build_function_signature_parameter_list())
from Module import PythonNamer
namer = PythonNamer()
print(tk.build_parser_idecl(namer=namer))
_print_empty_line()
block = CodeBlock.CodeBlock()
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
_print_empty_line()
_print_empty_line()
tk = TupleAndKeywords()
tk.add_parameter(Argument.Argument(Types.Type(("int", "*",), 1, "FundamentalType"), "foo", "nullptr"))
tk.add_parameter(Argument.Argument(Types.Type(("double", "&",), 2, "FundamentalType"), "bar", "PI"))
tk.add_parameter(Argument.Argument(Types.Type(("long unsigned int", "&", "const",), 3, "FundamentalType"), "xyz", "MAXINT"))
tk.add_parameter(Argument.Argument(Types.Type(("X", "const", "&",), 4, "Class"), "x", "_x"))
tk.add_parameter(Argument.Argument(Types.Type(("Y", "*",), 5, "Class"), "y", "_py"))
tk.add_parameter(Argument.Argument(Types.Type(("Z",), 6, "Class"), "z", "Z(1990)"))
tk.add_parameter(Argument.Argument(Types.Type(("bool",), 7, "FundamentalType"), "b", "true"))
tk.add_parameter(Argument.Argument(Types.Type(("wchar_t", "const", "*",), 8, "PointerType"), "str", 'L"Hello world!"'))
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
integer = Types.Type(("int",), 99, "FundamentalType")
Converters.add(Converters.ListConv(integer))
tk = TupleAndKeywords()
tk.add_parameter(Argument.Argument(Types.Type(("std::vector<int>", "const", "&",), 0, "Class"), "vi", "_vi"))
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
K = Types.Type(("wchar_t", "const", "*",), 111, "PointerType")
V = Types.Type(("wxColour", "*",), 112, "PointerType")
Converters.add(Converters.DictConv(K, V))
tk = TupleAndKeywords()
tk.add_parameter(Argument.Argument(Types.Type(("std::map<wchar_t const *, wxColour *>", "&",), 0, "Class"), "m"))
tk.write_args_parsing_code(block, namer, True, "return nullptr;", "<TEST>")
print(block.flush())
# Otherwise display the command help.
if len(sys.argv) < 3 or sys.argv[1] not in opt:
print("Help:\n"
"\t-c archive_name\t\tto create the archive (source file namen have"
" to be: " + GPS_FILE + ", " + CAMERA_FILE + ", " + CAN_FILE + ")\n"
"\t-x archive name\t\tto extract the archive\n")
sys.exit(1)
# Compress the files
if sys.argv[1] == '-c':
print("Creating the archive..")
if not (ARCHIVE_NAME[len(ARCHIVE_NAME) - 2:] == 'gz'):
ARCHIVE_NAME += ".gz"
Converters.correct_last_line_file(GPS_FILE)
Converters.correct_last_line_file(CAN_FILE)
if not os.path.isfile(OUTPUT_CAN_FILE):
Converters.convert_canframe_file(CAN_FILE, OUTPUT_CAN_FILE)
if not os.path.isfile(OUTPUT_CAMERA_FILE):
Converters.convert_video_to_mp4(CAMERA_FILE, OUTPUT_CAMERA_FILE)
c['gps'] = [l for l in open(GPS_FILE)]
c['can'] = [l for l in open(OUTPUT_CAN_FILE)]
c['camera'] = open(OUTPUT_CAMERA_FILE, 'rb').read()
with gzip.open(ARCHIVE_NAME, 'wb') as f:
f.write(pickle.dumps(c))
print("Archive created in " + ARCHIVE_NAME)
plt.savefig('./plots/Ground_time_SC' + simulation_file[-6:-4] + '.pdf')
plt.show()
#Importing Remaining Information
#-- pandas
group2bay_compliance = pd.read_csv(open(base_directory +
'/Bay Compliance.csv'),
sep=',')
bay_distances = pd.read_csv(open(base_directory + '/Bay Distances.csv'),
sep=',')
preferences = pd.read_csv(open(base_directory + '/Preference Table.csv'),
sep=',')
#-- csv to dictionary
aircraft_type2characteristics = CONV.csv2dict(
base_directory + '/Aircraft_type2characteristics.csv',
sep=',',
main_cat='AC Type')
all_bays = np.array(list(group2bay_compliance['Bay']))
#all_bays = np.array(list(group2bay_compliance[group2bay_compliance['total'] > 0]['Bay']))
print('Importing info: DONE \n')
result_files = [
'Bay Assignments Results 02-06-2015.csv',
'Bay Assignments Results 05-07-2015.csv'
]
appendix_result = pd.read_csv(open(base_directory + '/' + result_files[0]),
sep=',')
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