def __init__(self, iface):
"""Constructor.
:param iface: An interface instance that will be passed to this class
which provides the hook by which you can manipulate the QGIS
application at run time.
:type iface: QgsInterface
"""
# Save reference to the QGIS interface
self.iface = iface
# initialize plugin directory
self.plugin_dir = os.path.dirname(__file__)
# initialize locale
locale = QSettings().value('locale/userLocale')[0:2]
locale_path = os.path.join(self.plugin_dir, 'i18n',
'SpatialDM_{}.qm'.format(locale))
if os.path.exists(locale_path):
self.translator = QTranslator()
self.translator.load(locale_path)
if qVersion() > '4.3.3':
QCoreApplication.installTranslator(self.translator)
# Create the dialog (after translation) and keep reference
self.dlg = SpatialDMDialog()
# Declare instance attributes
self.actions = []
self.menu = self.tr(u'&SpatialDM')
# TODO: We are going to let the user set this up in a future iteration
self.toolbar = self.iface.addToolBar(u'SpatialDM')
self.toolbar.setObjectName(u'SpatialDM')
class SpatialDMDialogTest(unittest.TestCase):
"""Test dialog works."""
def setUp(self):
"""Runs before each test."""
self.dialog = SpatialDMDialog(None)
def tearDown(self):
"""Runs after each test."""
self.dialog = None
def test_dialog_ok(self):
"""Test we can click OK."""
button = self.dialog.button_box.button(QDialogButtonBox.Ok)
button.click()
result = self.dialog.result()
self.assertEqual(result, QDialog.Accepted)
def test_dialog_cancel(self):
"""Test we can click cancel."""
button = self.dialog.button_box.button(QDialogButtonBox.Cancel)
button.click()
result = self.dialog.result()
self.assertEqual(result, QDialog.Rejected)
class SpatialDMDialogTest(unittest.TestCase):
"""Test dialog works."""
def setUp(self):
"""Runs before each test."""
self.dialog = SpatialDMDialog(None)
def tearDown(self):
"""Runs after each test."""
self.dialog = None
def test_dialog_ok(self):
"""Test we can click OK."""
button = self.dialog.button_box.button(QDialogButtonBox.Ok)
button.click()
result = self.dialog.result()
self.assertEqual(result, QDialog.Accepted)
def test_dialog_cancel(self):
"""Test we can click cancel."""
button = self.dialog.button_box.button(QDialogButtonBox.Cancel)
button.click()
result = self.dialog.result()
self.assertEqual(result, QDialog.Rejected)
def __init__(self, iface):
"""Constructor.
:param iface: An interface instance that will be passed to this class
which provides the hook by which you can manipulate the QGIS
application at run time.
:type iface: QgsInterface
"""
# Save reference to the QGIS interface
self.iface = iface
# initialize plugin directory
self.plugin_dir = os.path.dirname(__file__)
# initialize locale
locale = QSettings().value('locale/userLocale')[0:2]
locale_path = os.path.join(
self.plugin_dir,
'i18n',
'SpatialDM_{}.qm'.format(locale))
if os.path.exists(locale_path):
self.translator = QTranslator()
self.translator.load(locale_path)
if qVersion() > '4.3.3':
QCoreApplication.installTranslator(self.translator)
# Create the dialog (after translation) and keep reference
self.dlg = SpatialDMDialog()
# Declare instance attributes
self.actions = []
self.menu = self.tr(u'&SpatialDM')
# TODO: We are going to let the user set this up in a future iteration
self.toolbar = self.iface.addToolBar(u'SpatialDM')
self.toolbar.setObjectName(u'SpatialDM')
class SpatialDM:
"""QGIS Plugin Implementation."""
def __init__(self, iface):
"""Constructor.
:param iface: An interface instance that will be passed to this class
which provides the hook by which you can manipulate the QGIS
application at run time.
:type iface: QgsInterface
"""
# Save reference to the QGIS interface
self.iface = iface
# initialize plugin directory
self.plugin_dir = os.path.dirname(__file__)
# initialize locale
locale = QSettings().value('locale/userLocale')[0:2]
locale_path = os.path.join(self.plugin_dir, 'i18n',
'SpatialDM_{}.qm'.format(locale))
if os.path.exists(locale_path):
self.translator = QTranslator()
self.translator.load(locale_path)
if qVersion() > '4.3.3':
QCoreApplication.installTranslator(self.translator)
# Create the dialog (after translation) and keep reference
self.dlg = SpatialDMDialog()
# Declare instance attributes
self.actions = []
self.menu = self.tr(u'&SpatialDM')
# TODO: We are going to let the user set this up in a future iteration
self.toolbar = self.iface.addToolBar(u'SpatialDM')
self.toolbar.setObjectName(u'SpatialDM')
# noinspection PyMethodMayBeStatic
def tr(self, message):
"""Get the translation for a string using Qt translation API.
We implement this ourselves since we do not inherit QObject.
:param message: String for translation.
:type message: str, QString
:returns: Translated version of message.
:rtype: QString
"""
# noinspection PyTypeChecker,PyArgumentList,PyCallByClass
return QCoreApplication.translate('SpatialDM', message)
def add_action(self,
icon_path,
text,
callback,
enabled_flag=True,
add_to_menu=True,
add_to_toolbar=True,
status_tip=None,
whats_this=None,
parent=None):
"""Add a toolbar icon to the toolbar.
:param icon_path: Path to the icon for this action. Can be a resource
path (e.g. ':/plugins/foo/bar.png') or a normal file system path.
:type icon_path: str
:param text: Text that should be shown in menu items for this action.
:type text: str
:param callback: Function to be called when the action is triggered.
:type callback: function
:param enabled_flag: A flag indicating if the action should be enabled
by default. Defaults to True.
:type enabled_flag: bool
:param add_to_menu: Flag indicating whether the action should also
be added to the menu. Defaults to True.
:type add_to_menu: bool
:param add_to_toolbar: Flag indicating whether the action should also
be added to the toolbar. Defaults to True.
:type add_to_toolbar: bool
:param status_tip: Optional text to show in a popup when mouse pointer
hovers over the action.
:type status_tip: str
:param parent: Parent widget for the new action. Defaults None.
:type parent: QWidget
:param whats_this: Optional text to show in the status bar when the
mouse pointer hovers over the action.
:returns: The action that was created. Note that the action is also
added to self.actions list.
:rtype: QAction
"""
icon = QIcon(icon_path)
action = QAction(icon, text, parent)
action.triggered.connect(callback)
action.setEnabled(enabled_flag)
if status_tip is not None:
action.setStatusTip(status_tip)
if whats_this is not None:
action.setWhatsThis(whats_this)
if add_to_toolbar:
self.toolbar.addAction(action)
if add_to_menu:
self.iface.addPluginToRasterMenu(self.menu, action)
self.actions.append(action)
return action
def initGui(self):
"""Create the menu entries and toolbar icons inside the QGIS GUI."""
icon_path = ':/plugins/SpatialDM/icon.png'
self.add_action(
icon_path,
text=self.tr(u'Run Data Mining Algorithms on Spatial Data'),
callback=self.run,
parent=self.iface.mainWindow())
self.initGUI()
self.defineSignals()
def unload(self):
"""Removes the plugin menu item and icon from QGIS GUI."""
for action in self.actions:
self.iface.removePluginRasterMenu(self.tr(u'&SpatialDM'), action)
self.iface.removeToolBarIcon(action)
# remove the toolbar
del self.toolbar
def initGUI(self):
self.dlg.raster_combo_box.clear()
self.dlg.bands_combo_box.clear()
self.dlg.csv_dir_text_edit.clear()
self.dlg.csv_checkBox.setEnabled(True)
self.dlg.csv_checkBox.setChecked(False)
self.initDTreeGUI()
self.initABoostGUI()
self.initRForestGUI()
self.chooseSplit()
def initDTreeGUI(self):
self.dlg.criterion_combo_box.clear()
self.dlg.splitter_combo_box.clear()
self.criterion_list = ['gini', 'entropy']
self.splitter_list = ['best', 'random']
self.dlg.criterion_combo_box.addItems(self.criterion_list)
self.dlg.splitter_combo_box.addItems(self.splitter_list)
def initABoostGUI(self):
self.dlg.algorithm_combo_box.clear()
self.algorithm_list = ['SAMME.R', 'SAMME']
self.dlg.algorithm_combo_box.addItems(self.algorithm_list)
def initRForestGUI(self):
self.dlg.criterion_combo_box_3.clear()
self.dlg.splitter_combo_box_3.clear()
self.criterion_list = ['gini', 'entropy']
self.splitter_list = ['best', 'random']
self.dlg.criterion_combo_box_3.addItems(self.criterion_list)
self.dlg.splitter_combo_box_3.addItems(self.splitter_list)
def chooseInputType(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if (csv_state):
self.dlg.raster_combo_box.setEnabled(False)
self.dlg.csv_dir_text_edit.setEnabled(True)
self.dlg.csv_dir_browse_button.setEnabled(True)
else:
self.dlg.raster_combo_box.setEnabled(True)
self.dlg.csv_dir_text_edit.setEnabled(False)
self.dlg.csv_dir_browse_button.setEnabled(False)
self.updateBands()
def updateBands(self):
self.dlg.bands_combo_box.clear()
csv_state = self.dlg.csv_checkBox.isChecked()
if (csv_state):
if (self.dlg.csv_dir_text_edit.toPlainText() == ""):
return
r = csv.reader(open(self.dlg.csv_dir_text_edit.toPlainText()))
self.csv_band_list = r.next()
self.bandCount = len(self.csv_band_list)
self.dlg.bands_combo_box.addItems(self.csv_band_list)
else:
currIndex = self.dlg.raster_combo_box.currentIndex()
if (currIndex == -1):
return
raster_layer = self.layer_list[currIndex]
self.bandCount = raster_layer.bandCount()
band_name_list = []
for i in range(0, self.bandCount):
band_name_list.append(raster_layer.bandName(i + 1))
self.dlg.bands_combo_box.addItems(band_name_list)
def chooseSplit(self):
split_state = self.dlg.partition_checkBox.isChecked()
if (split_state):
self.dlg.split_perc_spinBox.setEnabled(True)
self.dlg.disp_accu_checkBox.setEnabled(True)
self.dlg.label_12.setEnabled(True)
else:
self.dlg.split_perc_spinBox.setEnabled(False)
self.dlg.disp_accu_checkBox.setEnabled(False)
self.dlg.label_12.setEnabled(False)
def defineSignals(self):
QObject.connect(self.dlg.raster_combo_box,
SIGNAL("currentIndexChanged ( int )"),
self.updateBands)
QObject.connect(self.dlg.csv_dir_text_edit, SIGNAL("textChanged ()"),
self.updateBands)
QObject.connect(self.dlg.csv_checkBox, SIGNAL("stateChanged ( int )"),
self.chooseInputType)
QObject.connect(self.dlg.csv_dir_browse_button,
SIGNAL("clicked ( bool )"), self.browseFile)
QObject.connect(self.dlg.train_pushButton, SIGNAL("clicked ( bool )"),
self.trainDecisionTree)
QObject.connect(self.dlg.train_pushButton_2,
SIGNAL("clicked ( bool )"), self.trainAdaBoost)
QObject.connect(self.dlg.train_pushButton_3,
SIGNAL("clicked ( bool )"), self.trainRandomForest)
QObject.connect(self.dlg.partition_checkBox,
SIGNAL("stateChanged ( int )"), self.chooseSplit)
def browseFile(self):
fname = QFileDialog.getOpenFileName(
None, "Select CSV File",
os.path.join(self.plugin_dir, 'test_datasets'), "*.csv *.txt")
self.dlg.csv_dir_text_edit.setText(fname)
def getTrainingDatafromCSV(self):
f = open(self.dlg.csv_dir_text_edit.toPlainText())
r = csv.reader(f)
target_band = self.dlg.bands_combo_box.currentIndex()
data_cols = []
for i in range(0, self.bandCount):
if (i != target_band):
data_cols.append(i)
self.train_data = []
self.train_target = []
self.test_data = []
self.test_target = []
self.data = []
self.target = []
for row in r:
self.train_data.append(list(row[i] for i in data_cols))
self.train_target.append(row[target_band])
self.train_data.pop(0)
self.train_target.pop(0)
self.data = self.train_data
self.target = self.train_target
split_state = self.dlg.partition_checkBox.isChecked()
if (split_state):
test_data_cnt = round(
len(self.train_data) *
(1 - self.dlg.split_perc_spinBox.value() / 100.0))
while (len(self.test_data) < test_data_cnt):
idx = random.randint(0, len(self.train_data) - 1)
self.test_data.append(self.train_data[idx])
self.test_target.append(self.train_target[idx])
self.train_data.pop(idx)
self.train_target.pop(idx)
def getTrainingDatafromRaster(self):
currIndex = self.dlg.raster_combo_box.currentIndex()
raster_layer = self.layer_list[currIndex]
target_band = self.dlg.bands_combo_box.currentIndex() + 1
w = raster_layer.width()
h = raster_layer.height()
self.train_data = []
self.train_target = []
self.test_data = []
self.test_target = []
self.data = []
self.target = []
for i in range(0, w):
for j in range(0, h):
instance = []
for k in range(1, self.bandCount + 1):
pos = QgsPoint(i, -j)
ident = raster_layer.dataProvider().identify(
pos, QgsRaster.IdentifyFormatValue)
if (k == target_band):
self.train_target.append(ident.results().get(k))
else:
instance.append(ident.results().get(k))
self.train_data.append(instance)
self.data = self.train_data
self.target = self.train_target
split_state = self.dlg.partition_checkBox.isChecked()
if (split_state):
test_data_cnt = round(
len(self.train_data) *
(1 - self.dlg.split_perc_spinBox.value() / 100.0))
while (len(self.test_data) < test_data_cnt):
idx = random.randint(0, len(self.train_data) - 1)
self.test_data.append(self.train_data[idx])
self.test_target.append(self.train_target[idx])
self.train_data.pop(idx)
self.train_target.pop(idx)
def run(self):
"""Run method that performs all the real work"""
self.initGUI()
layers = self.iface.legendInterface().layers()
self.layer_list = []
self.layer_name_list = []
for layer in layers:
if (str(type(layer)) == "<class 'qgis._core.QgsRasterLayer'>"):
self.layer_list.append(layer)
self.layer_name_list.append(layer.name())
self.dlg.raster_combo_box.addItems(self.layer_name_list)
if (len(self.layer_list) == 0):
self.dlg.csv_checkBox.setChecked(True)
self.dlg.csv_checkBox.setEnabled(False)
self.chooseInputType()
# show the dialog
self.dlg.show()
# Run the dialog event loop
result = self.dlg.exec_()
# See if OK was pressed
if result:
# Do something useful here - delete the line containing pass and
# substitute with your code.
pass
def showConfusionMatrix(self, clf):
self.predict = clf.predict(self.data)
conf_matrix = confusion_matrix(self.target, self.predict)
s = [[str(e) for e in row] for row in conf_matrix]
lens = [max(map(len, col)) for col in zip(*s)]
fmt = '\t'.join('{{:{}}}'.format(x) for x in lens)
table = [fmt.format(*row) for row in s]
QMessageBox.information(self.dlg, "Confusion Matrix",
"%s" % ('\n'.join(table)))
# print '\n'.join(table)
# print conf_matrix
def trainDecisionTree(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if (csv_state):
self.getTrainingDatafromCSV()
else:
self.getTrainingDatafromRaster()
idx = self.dlg.criterion_combo_box.currentIndex()
self.criterion = self.criterion_list[idx]
idx = self.dlg.splitter_combo_box.currentIndex()
self.splitter = self.splitter_list[idx]
self.max_depth = None if (self.dlg.max_depth_spinBox.value() == 0
) else self.dlg.max_depth_spinBox.value()
self.min_samples_split = self.dlg.min_samples_split_spinBox.value()
self.min_samples_leaf = self.dlg.min_samples_leaf_spinBox.value()
self.min_weight_fraction_leaf = self.dlg.min_weight_fraction_leaf_spinBox.value(
)
self.max_leaf_nodes = None if (
self.dlg.max_leaf_nodes_spinBox.value()
== 0) else self.dlg.max_leaf_nodes_spinBox.value()
clf = tree.DecisionTreeClassifier(\
criterion = self.criterion, \
splitter = self.splitter, \
max_depth = self.max_depth, \
min_samples_split = self.min_samples_split, \
min_samples_leaf = self.min_samples_leaf, \
min_weight_fraction_leaf = self.min_weight_fraction_leaf, \
max_leaf_nodes = self.max_leaf_nodes)
clf = clf.fit(self.train_data, self.train_target)
split_state = self.dlg.partition_checkBox.isChecked()
disp_acc = self.dlg.disp_accu_checkBox.isChecked()
if (split_state and disp_acc):
train_acc = clf.score(self.train_data, self.train_target) * 100
test_acc = clf.score(self.test_data, self.test_target) * 100
QMessageBox.information(
self.dlg, "Accuracy",
"Training Data Accuracy: %.2f%c\nTest Data Accuracy: %.2f%c" %
(train_acc, '%', test_acc, '%'))
else:
train_acc = clf.score(self.train_data, self.train_target) * 100
QMessageBox.information(self.dlg, "Accuracy",
"Accuracy: %.2f%%" % (train_acc))
checkstate = self.dlg.conf_matrix_combo_box.isChecked()
if (checkstate):
self.showConfusionMatrix(clf)
output_file_path = os.path.join(self.plugin_dir, 'tree.dot')
tree.export_graphviz(clf, out_file=output_file_path)
# graph = pydot.graph_from_dot_file(output_file_path)
# graph.write_png(os.path.join(self.plugin_dir, 'output.png'))
# os.system('dot -Tpng ' + os.path.join(self.plugin_dir, 'tree.dot')\
# + ' -o ' + os.path.join(self.plugin_dir, 'output.png'))
os.system('open ' + os.path.join(self.plugin_dir, 'tree.dot'))
def trainAdaBoost(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if (csv_state):
self.getTrainingDatafromCSV()
else:
self.getTrainingDatafromRaster()
idx = self.dlg.algorithm_combo_box.currentIndex()
self.algorithm = self.algorithm_list[idx]
self.n_estimators = self.dlg.n_estimators_spinBox.value()
self.learning_rate = self.dlg.learning_rate_spinBox.value()
# self.random_state = None if (self.dlg.random_state_spinBox.value() == 0) else self.dlg.random_state_spinBox.value()
clf = ensemble.AdaBoostClassifier(\
n_estimators = self.n_estimators, \
learning_rate = self.learning_rate, \
algorithm = self.algorithm)
# random_state = self.random_state)
clf = clf.fit(self.train_data, self.train_target)
split_state = self.dlg.partition_checkBox.isChecked()
disp_acc = self.dlg.disp_accu_checkBox.isChecked()
if (split_state and disp_acc):
train_acc = clf.score(self.train_data, self.train_target) * 100
test_acc = clf.score(self.test_data, self.test_target) * 100
QMessageBox.information(
self.dlg, "Accuracy",
"Training Data Accuracy: %.2f%c\nTest Data Accuracy: %.2f%c" %
(train_acc, '%', test_acc, '%'))
else:
train_acc = clf.score(self.train_data, self.train_target) * 100
QMessageBox.information(self.dlg, "Accuracy",
"Accuracy: %.2f%%" % (train_acc))
checkstate = self.dlg.conf_matrix_combo_box.isChecked()
if (checkstate):
self.showConfusionMatrix(clf)
def trainRandomForest(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if (csv_state):
self.getTrainingDatafromCSV()
else:
self.getTrainingDatafromRaster()
idx = self.dlg.criterion_combo_box_3.currentIndex()
self.criterion = self.criterion_list[idx]
idx = self.dlg.splitter_combo_box_3.currentIndex()
self.splitter = self.splitter_list[idx]
self.max_depth = None if (self.dlg.max_depth_spinBox_3.value() == 0
) else self.dlg.max_depth_spinBox_3.value()
self.min_samples_split = self.dlg.min_samples_split_spinBox_3.value()
self.min_samples_leaf = self.dlg.min_samples_leaf_spinBox_3.value()
self.min_weight_fraction_leaf = self.dlg.min_weight_fraction_leaf_spinBox_3.value(
)
self.max_leaf_nodes = None if (
self.dlg.max_leaf_nodes_spinBox_3.value()
== 0) else self.dlg.max_leaf_nodes_spinBox_3.value()
self.bootstrap = self.dlg.bootstrap_checkBox.isChecked()
self.oob_score = self.dlg.oob_score_checkBox.isChecked()
self.warm_start = self.dlg.warm_start_checkBox.isChecked()
self.n_jobs = self.dlg.n_jobs_spinBox.value()
clf = ensemble.RandomForestClassifier(\
criterion = self.criterion, \
max_depth = self.max_depth, \
min_samples_split = self.min_samples_split, \
min_samples_leaf = self.min_samples_leaf, \
min_weight_fraction_leaf = self.min_weight_fraction_leaf, \
max_leaf_nodes = self.max_leaf_nodes, \
bootstrap = self.bootstrap, \
oob_score = self.oob_score, \
n_jobs = self.n_jobs, \
warm_start = self.warm_start)
clf = clf.fit(self.train_data, self.train_target)
split_state = self.dlg.partition_checkBox.isChecked()
disp_acc = self.dlg.disp_accu_checkBox.isChecked()
if (split_state and disp_acc):
train_acc = clf.score(self.train_data, self.train_target) * 100
test_acc = clf.score(self.test_data, self.test_target) * 100
QMessageBox.information(
self.dlg, "Accuracy",
"Training Data Accuracy: %.2f%c\nTest Data Accuracy: %.2f%c" %
(train_acc, '%', test_acc, '%'))
else:
train_acc = clf.score(self.train_data, self.train_target) * 100
QMessageBox.information(self.dlg, "Accuracy",
"Accuracy: %.2f%%" % (train_acc))
checkstate = self.dlg.conf_matrix_combo_box.isChecked()
if (checkstate):
self.showConfusionMatrix(clf)
def setUp(self):
"""Runs before each test."""
self.dialog = SpatialDMDialog(None)
class SpatialDM:
"""QGIS Plugin Implementation."""
def __init__(self, iface):
"""Constructor.
:param iface: An interface instance that will be passed to this class
which provides the hook by which you can manipulate the QGIS
application at run time.
:type iface: QgsInterface
"""
# Save reference to the QGIS interface
self.iface = iface
# initialize plugin directory
self.plugin_dir = os.path.dirname(__file__)
# initialize locale
locale = QSettings().value('locale/userLocale')[0:2]
locale_path = os.path.join(
self.plugin_dir,
'i18n',
'SpatialDM_{}.qm'.format(locale))
if os.path.exists(locale_path):
self.translator = QTranslator()
self.translator.load(locale_path)
if qVersion() > '4.3.3':
QCoreApplication.installTranslator(self.translator)
# Create the dialog (after translation) and keep reference
self.dlg = SpatialDMDialog()
# Declare instance attributes
self.actions = []
self.menu = self.tr(u'&SpatialDM')
# TODO: We are going to let the user set this up in a future iteration
self.toolbar = self.iface.addToolBar(u'SpatialDM')
self.toolbar.setObjectName(u'SpatialDM')
# noinspection PyMethodMayBeStatic
def tr(self, message):
"""Get the translation for a string using Qt translation API.
We implement this ourselves since we do not inherit QObject.
:param message: String for translation.
:type message: str, QString
:returns: Translated version of message.
:rtype: QString
"""
# noinspection PyTypeChecker,PyArgumentList,PyCallByClass
return QCoreApplication.translate('SpatialDM', message)
def add_action(
self,
icon_path,
text,
callback,
enabled_flag=True,
add_to_menu=True,
add_to_toolbar=True,
status_tip=None,
whats_this=None,
parent=None):
"""Add a toolbar icon to the toolbar.
:param icon_path: Path to the icon for this action. Can be a resource
path (e.g. ':/plugins/foo/bar.png') or a normal file system path.
:type icon_path: str
:param text: Text that should be shown in menu items for this action.
:type text: str
:param callback: Function to be called when the action is triggered.
:type callback: function
:param enabled_flag: A flag indicating if the action should be enabled
by default. Defaults to True.
:type enabled_flag: bool
:param add_to_menu: Flag indicating whether the action should also
be added to the menu. Defaults to True.
:type add_to_menu: bool
:param add_to_toolbar: Flag indicating whether the action should also
be added to the toolbar. Defaults to True.
:type add_to_toolbar: bool
:param status_tip: Optional text to show in a popup when mouse pointer
hovers over the action.
:type status_tip: str
:param parent: Parent widget for the new action. Defaults None.
:type parent: QWidget
:param whats_this: Optional text to show in the status bar when the
mouse pointer hovers over the action.
:returns: The action that was created. Note that the action is also
added to self.actions list.
:rtype: QAction
"""
icon = QIcon(icon_path)
action = QAction(icon, text, parent)
action.triggered.connect(callback)
action.setEnabled(enabled_flag)
if status_tip is not None:
action.setStatusTip(status_tip)
if whats_this is not None:
action.setWhatsThis(whats_this)
if add_to_toolbar:
self.toolbar.addAction(action)
if add_to_menu:
self.iface.addPluginToRasterMenu(
self.menu,
action)
self.actions.append(action)
return action
def initGui(self):
"""Create the menu entries and toolbar icons inside the QGIS GUI."""
icon_path = ':/plugins/SpatialDM/icon.png'
self.add_action(
icon_path,
text=self.tr(u'Run Data Mining Algorithms on Spatial Data'),
callback=self.run,
parent=self.iface.mainWindow())
self.initGUI()
self.defineSignals()
def unload(self):
"""Removes the plugin menu item and icon from QGIS GUI."""
for action in self.actions:
self.iface.removePluginRasterMenu(
self.tr(u'&SpatialDM'),
action)
self.iface.removeToolBarIcon(action)
# remove the toolbar
del self.toolbar
def initGUI(self):
self.dlg.raster_combo_box.clear()
self.dlg.bands_combo_box.clear()
self.dlg.csv_dir_text_edit.clear()
self.dlg.csv_checkBox.setEnabled(True)
self.dlg.csv_checkBox.setChecked(False)
self.initDTreeGUI()
self.initABoostGUI()
self.initRForestGUI()
self.chooseSplit()
def initDTreeGUI(self):
self.dlg.criterion_combo_box.clear()
self.dlg.splitter_combo_box.clear()
self.criterion_list = ['gini', 'entropy']
self.splitter_list = ['best', 'random']
self.dlg.criterion_combo_box.addItems(self.criterion_list)
self.dlg.splitter_combo_box.addItems(self.splitter_list)
def initABoostGUI(self):
self.dlg.algorithm_combo_box.clear()
self.algorithm_list = ['SAMME.R', 'SAMME']
self.dlg.algorithm_combo_box.addItems(self.algorithm_list)
def initRForestGUI(self):
self.dlg.criterion_combo_box_3.clear()
self.dlg.splitter_combo_box_3.clear()
self.criterion_list = ['gini', 'entropy']
self.splitter_list = ['best', 'random']
self.dlg.criterion_combo_box_3.addItems(self.criterion_list)
self.dlg.splitter_combo_box_3.addItems(self.splitter_list)
def chooseInputType(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if(csv_state):
self.dlg.raster_combo_box.setEnabled(False)
self.dlg.csv_dir_text_edit.setEnabled(True)
self.dlg.csv_dir_browse_button.setEnabled(True)
else:
self.dlg.raster_combo_box.setEnabled(True)
self.dlg.csv_dir_text_edit.setEnabled(False)
self.dlg.csv_dir_browse_button.setEnabled(False)
self.updateBands()
def updateBands(self):
self.dlg.bands_combo_box.clear()
csv_state = self.dlg.csv_checkBox.isChecked()
if(csv_state):
if(self.dlg.csv_dir_text_edit.toPlainText() == ""):
return
r = csv.reader(open(self.dlg.csv_dir_text_edit.toPlainText()))
self.csv_band_list = r.next()
self.bandCount = len(self.csv_band_list)
self.dlg.bands_combo_box.addItems(self.csv_band_list)
else:
currIndex = self.dlg.raster_combo_box.currentIndex()
if(currIndex == -1):
return
raster_layer = self.layer_list[currIndex]
self.bandCount = raster_layer.bandCount()
band_name_list = []
for i in range(0,self.bandCount):
band_name_list.append(raster_layer.bandName(i + 1))
self.dlg.bands_combo_box.addItems(band_name_list)
def chooseSplit(self):
split_state = self.dlg.partition_checkBox.isChecked()
if(split_state):
self.dlg.split_perc_spinBox.setEnabled(True)
self.dlg.disp_accu_checkBox.setEnabled(True)
self.dlg.label_12.setEnabled(True)
else:
self.dlg.split_perc_spinBox.setEnabled(False)
self.dlg.disp_accu_checkBox.setEnabled(False)
self.dlg.label_12.setEnabled(False)
def defineSignals(self):
QObject.connect(self.dlg.raster_combo_box, SIGNAL("currentIndexChanged ( int )"), self.updateBands)
QObject.connect(self.dlg.csv_dir_text_edit, SIGNAL("textChanged ()"), self.updateBands)
QObject.connect(self.dlg.csv_checkBox, SIGNAL("stateChanged ( int )"), self.chooseInputType)
QObject.connect(self.dlg.csv_dir_browse_button, SIGNAL("clicked ( bool )"), self.browseFile)
QObject.connect(self.dlg.train_pushButton, SIGNAL("clicked ( bool )"), self.trainDecisionTree)
QObject.connect(self.dlg.train_pushButton_2, SIGNAL("clicked ( bool )"), self.trainAdaBoost)
QObject.connect(self.dlg.train_pushButton_3, SIGNAL("clicked ( bool )"), self.trainRandomForest)
QObject.connect(self.dlg.partition_checkBox, SIGNAL("stateChanged ( int )"), self.chooseSplit)
def browseFile(self):
fname = QFileDialog.getOpenFileName(None, "Select CSV File", os.path.join(self.plugin_dir, 'test_datasets'), "*.csv *.txt")
self.dlg.csv_dir_text_edit.setText(fname)
def getTrainingDatafromCSV(self):
f = open(self.dlg.csv_dir_text_edit.toPlainText())
r = csv.reader(f)
target_band = self.dlg.bands_combo_box.currentIndex()
data_cols = []
for i in range(0,self.bandCount):
if(i != target_band):
data_cols.append(i)
self.train_data = []
self.train_target = []
self.test_data = []
self.test_target = []
self.data = []
self.target = []
for row in r:
self.train_data.append(list(row[i] for i in data_cols))
self.train_target.append(row[target_band])
self.train_data.pop(0)
self.train_target.pop(0)
self.data = self.train_data
self.target = self.train_target
split_state = self.dlg.partition_checkBox.isChecked()
if(split_state):
test_data_cnt = round(len(self.train_data)*(1 - self.dlg.split_perc_spinBox.value()/100.0))
while(len(self.test_data) < test_data_cnt):
idx = random.randint(0, len(self.train_data) - 1)
self.test_data.append(self.train_data[idx])
self.test_target.append(self.train_target[idx])
self.train_data.pop(idx)
self.train_target.pop(idx)
def getTrainingDatafromRaster(self):
currIndex = self.dlg.raster_combo_box.currentIndex()
raster_layer = self.layer_list[currIndex]
target_band = self.dlg.bands_combo_box.currentIndex() + 1
w = raster_layer.width()
h = raster_layer.height()
self.train_data = []
self.train_target = []
self.test_data = []
self.test_target = []
self.data = []
self.target = []
for i in range(0,w):
for j in range(0,h):
instance = []
for k in range(1,self.bandCount + 1):
pos = QgsPoint(i, -j)
ident = raster_layer.dataProvider().identify(pos, QgsRaster.IdentifyFormatValue)
if(k == target_band):
self.train_target.append(ident.results().get(k))
else:
instance.append(ident.results().get(k))
self.train_data.append(instance)
self.data = self.train_data
self.target = self.train_target
split_state = self.dlg.partition_checkBox.isChecked()
if(split_state):
test_data_cnt = round(len(self.train_data)*(1 - self.dlg.split_perc_spinBox.value()/100.0))
while(len(self.test_data) < test_data_cnt):
idx = random.randint(0, len(self.train_data) - 1)
self.test_data.append(self.train_data[idx])
self.test_target.append(self.train_target[idx])
self.train_data.pop(idx)
self.train_target.pop(idx)
def run(self):
"""Run method that performs all the real work"""
self.initGUI()
layers = self.iface.legendInterface().layers()
self.layer_list = []
self.layer_name_list = []
for layer in layers:
if(str(type(layer)) == "<class 'qgis._core.QgsRasterLayer'>"):
self.layer_list.append(layer)
self.layer_name_list.append(layer.name())
self.dlg.raster_combo_box.addItems(self.layer_name_list)
if(len(self.layer_list) == 0):
self.dlg.csv_checkBox.setChecked(True)
self.dlg.csv_checkBox.setEnabled(False)
self.chooseInputType()
# show the dialog
self.dlg.show()
# Run the dialog event loop
result = self.dlg.exec_()
# See if OK was pressed
if result:
# Do something useful here - delete the line containing pass and
# substitute with your code.
pass
def showConfusionMatrix(self, clf):
self.predict = clf.predict(self.data)
conf_matrix = confusion_matrix(self.target, self.predict)
s = [[str(e) for e in row] for row in conf_matrix]
lens = [max(map(len, col)) for col in zip(*s)]
fmt = '\t'.join('{{:{}}}'.format(x) for x in lens)
table = [fmt.format(*row) for row in s]
QMessageBox.information(self.dlg, "Confusion Matrix", "%s" % ('\n'.join(table)))
# print '\n'.join(table)
# print conf_matrix
def trainDecisionTree(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if(csv_state):
self.getTrainingDatafromCSV()
else:
self.getTrainingDatafromRaster()
idx = self.dlg.criterion_combo_box.currentIndex()
self.criterion = self.criterion_list[idx]
idx = self.dlg.splitter_combo_box.currentIndex()
self.splitter = self.splitter_list[idx]
self.max_depth = None if (self.dlg.max_depth_spinBox.value() == 0) else self.dlg.max_depth_spinBox.value()
self.min_samples_split = self.dlg.min_samples_split_spinBox.value()
self.min_samples_leaf = self.dlg.min_samples_leaf_spinBox.value()
self.min_weight_fraction_leaf = self.dlg.min_weight_fraction_leaf_spinBox.value()
self.max_leaf_nodes = None if (self.dlg.max_leaf_nodes_spinBox.value() == 0) else self.dlg.max_leaf_nodes_spinBox.value()
clf = tree.DecisionTreeClassifier(\
criterion = self.criterion, \
splitter = self.splitter, \
max_depth = self.max_depth, \
min_samples_split = self.min_samples_split, \
min_samples_leaf = self.min_samples_leaf, \
min_weight_fraction_leaf = self.min_weight_fraction_leaf, \
max_leaf_nodes = self.max_leaf_nodes)
clf = clf.fit(self.train_data, self.train_target)
split_state = self.dlg.partition_checkBox.isChecked()
disp_acc = self.dlg.disp_accu_checkBox.isChecked()
if(split_state and disp_acc):
train_acc = clf.score(self.train_data, self.train_target)*100
test_acc = clf.score(self.test_data, self.test_target)*100
QMessageBox.information(self.dlg, "Accuracy", "Training Data Accuracy: %.2f%c\nTest Data Accuracy: %.2f%c" % (train_acc, '%', test_acc, '%'))
else:
train_acc = clf.score(self.train_data, self.train_target)*100
QMessageBox.information(self.dlg, "Accuracy", "Accuracy: %.2f%%" % (train_acc))
checkstate = self.dlg.conf_matrix_combo_box.isChecked()
if(checkstate):
self.showConfusionMatrix(clf)
output_file_path = os.path.join(self.plugin_dir, 'tree.dot')
tree.export_graphviz(clf, out_file = output_file_path)
# graph = pydot.graph_from_dot_file(output_file_path)
# graph.write_png(os.path.join(self.plugin_dir, 'output.png'))
# os.system('dot -Tpng ' + os.path.join(self.plugin_dir, 'tree.dot')\
# + ' -o ' + os.path.join(self.plugin_dir, 'output.png'))
os.system('open ' + os.path.join(self.plugin_dir, 'tree.dot'))
def trainAdaBoost(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if(csv_state):
self.getTrainingDatafromCSV()
else:
self.getTrainingDatafromRaster()
idx = self.dlg.algorithm_combo_box.currentIndex()
self.algorithm = self.algorithm_list[idx];
self.n_estimators = self.dlg.n_estimators_spinBox.value()
self.learning_rate = self.dlg.learning_rate_spinBox.value()
# self.random_state = None if (self.dlg.random_state_spinBox.value() == 0) else self.dlg.random_state_spinBox.value()
clf = ensemble.AdaBoostClassifier(\
n_estimators = self.n_estimators, \
learning_rate = self.learning_rate, \
algorithm = self.algorithm)
# random_state = self.random_state)
clf = clf.fit(self.train_data, self.train_target)
split_state = self.dlg.partition_checkBox.isChecked()
disp_acc = self.dlg.disp_accu_checkBox.isChecked()
if(split_state and disp_acc):
train_acc = clf.score(self.train_data, self.train_target)*100
test_acc = clf.score(self.test_data, self.test_target)*100
QMessageBox.information(self.dlg, "Accuracy", "Training Data Accuracy: %.2f%c\nTest Data Accuracy: %.2f%c" % (train_acc, '%', test_acc, '%'))
else:
train_acc = clf.score(self.train_data, self.train_target)*100
QMessageBox.information(self.dlg, "Accuracy", "Accuracy: %.2f%%" % (train_acc))
checkstate = self.dlg.conf_matrix_combo_box.isChecked()
if(checkstate):
self.showConfusionMatrix(clf)
def trainRandomForest(self):
csv_state = self.dlg.csv_checkBox.isChecked()
if(csv_state):
self.getTrainingDatafromCSV()
else:
self.getTrainingDatafromRaster()
idx = self.dlg.criterion_combo_box_3.currentIndex()
self.criterion = self.criterion_list[idx]
idx = self.dlg.splitter_combo_box_3.currentIndex()
self.splitter = self.splitter_list[idx]
self.max_depth = None if (self.dlg.max_depth_spinBox_3.value() == 0) else self.dlg.max_depth_spinBox_3.value()
self.min_samples_split = self.dlg.min_samples_split_spinBox_3.value()
self.min_samples_leaf = self.dlg.min_samples_leaf_spinBox_3.value()
self.min_weight_fraction_leaf = self.dlg.min_weight_fraction_leaf_spinBox_3.value()
self.max_leaf_nodes = None if (self.dlg.max_leaf_nodes_spinBox_3.value() == 0) else self.dlg.max_leaf_nodes_spinBox_3.value()
self.bootstrap = self.dlg.bootstrap_checkBox.isChecked()
self.oob_score = self.dlg.oob_score_checkBox.isChecked()
self.warm_start = self.dlg.warm_start_checkBox.isChecked()
self.n_jobs = self.dlg.n_jobs_spinBox.value()
clf = ensemble.RandomForestClassifier(\
criterion = self.criterion, \
max_depth = self.max_depth, \
min_samples_split = self.min_samples_split, \
min_samples_leaf = self.min_samples_leaf, \
min_weight_fraction_leaf = self.min_weight_fraction_leaf, \
max_leaf_nodes = self.max_leaf_nodes, \
bootstrap = self.bootstrap, \
oob_score = self.oob_score, \
n_jobs = self.n_jobs, \
warm_start = self.warm_start)
clf = clf.fit(self.train_data, self.train_target)
split_state = self.dlg.partition_checkBox.isChecked()
disp_acc = self.dlg.disp_accu_checkBox.isChecked()
if(split_state and disp_acc):
train_acc = clf.score(self.train_data, self.train_target)*100
test_acc = clf.score(self.test_data, self.test_target)*100
QMessageBox.information(self.dlg, "Accuracy", "Training Data Accuracy: %.2f%c\nTest Data Accuracy: %.2f%c" % (train_acc, '%', test_acc, '%'))
else:
train_acc = clf.score(self.train_data, self.train_target)*100
QMessageBox.information(self.dlg, "Accuracy", "Accuracy: %.2f%%" % (train_acc))
checkstate = self.dlg.conf_matrix_combo_box.isChecked()
if(checkstate):
self.showConfusionMatrix(clf)
def setUp(self):
"""Runs before each test."""
self.dialog = SpatialDMDialog(None)
