def initMenus(self):
""" Initialise top bar menu items by importing from configuration file.
"""
menu_items = eval(file_io.load_config(MENU_FILE))
menubar = self.menuBar()
for menu in menu_items:
newMenu = menubar.addMenu(menu[0])
for action in menu[1]:
if action["name"] == "sep":
newMenu.addSeparator()
continue
newAction = QtGui.QAction(action["name"], self)
newAction.setShortcut(action["shortcut"])
newAction.setStatusTip(action["tip"])
newAction.triggered.connect(action["cb"])
newMenu.addAction(newAction)
import sys
from datetime import datetime
from sklearn.model_selection import RandomizedSearchCV
import file_io as io
import report as rp
import util as ut
# Display progress logs on stdout
log.basicConfig(level=log.INFO, format='%(asctime)s %(levelname)s %(message)s')
if __name__ == '__main__':
"""Does hp search and stores the parameters for each dataset and classifier."""
config = io.load_config(sys.argv, None)
experiment_dir = "{}_hpsearch".format(config['experiment'])
if not os.path.exists(experiment_dir):
os.makedirs(experiment_dir)
for dataset_i, dataset_filename in enumerate(config['datasets']):
log.debug("DATASET_{}: {}".format(dataset_i, dataset_filename))
# load preprocessed dataset
X, y, arff_data = io.load_data(dataset_filename, config)
dataset_name = os.path.splitext(dataset_filename)[0]
log.info("DATASET_{}_NAME: {}".format(dataset_i, dataset_name))
for estimator_i, estimator in enumerate(config['estimators']):
log.debug("ESTIMATOR_{}: {}".format(estimator_i,
import file_io
import arm
import simulator
import sequencer
import controls
import sys
from PyQt4 import QtGui, QtCore, QtOpenGL
MENU_FILE = 'menus.conf'
CONFIG_FILE = 'config.conf'
CONFIG = eval(file_io.load_config(CONFIG_FILE))
class MainWindow(QtGui.QMainWindow):
""" Main top level class, contains instances of all other main classes"""
def __init__(self):
""" Initialise all main classes.
"""
super(MainWindow, self).__init__()
self.file_manager = file_io.FileManager(self,
CONFIG["def_arm"],
CONFIG["def_arms_directory"])
self.arm = arm.Arm(self)
self.arm_data = None
self.sim_widget = simulator.SimWidget(self, self.arm, CONFIG["cam_config"])
self.controls_area = controls.ControlsArea(self)
Available estimators:
* RandomForestClassifier
* ExtraTreesClassifier
* DecisionTreeClassifier
* AdaBoostClassifier
* GaussianNB
* KNeighborsClassifier
* SVC
* GaussianProcessClassifier
* MLPClassifier
* Perceptron
* ...
TODO add more estimators
"""
config = io.load_config(sys.argv, default_config)
experiment_dir = config['experiment']
if not os.path.exists(experiment_dir):
os.makedirs(experiment_dir)
times_columns = [
'dataset_name', 'estimator_name', 'train_size', 'repetition', 'split',
'fit_time', 'score_time'
]
times = pd.DataFrame(columns=times_columns)
scores_columns = [
'dataset_name', 'estimator_name', 'train_size', 'repetition', 'split',
'accuracy', 'f1_macro', 'precision_macro', 'recall_macro'
]
scores = pd.DataFrame(columns=scores_columns)
