def handle_debug_button(self, index):
y = 10
print 'Debug button clicked : ' + str(index)
d = mg.load_dataset('table_B')
model = DataModel(d)
view = MTableView(model)
view.show()
import magellan as mg
import numpy as np
A = mg.load_dataset('table_A')
B = mg.load_dataset('table_B')
ob = mg.OverlapBlocker()
C = ob.block_tables(A,
B,
'address',
'address',
word_level=False,
qgram=3,
overlap_size=3,
l_output_attrs=['name', 'name'])
D = ob.block_candset(C, 'name', 'name', overlap_size=1)
E = ob.block_tuples(A.ix[1], B.ix[1], 'name', 'name', overlap_size=1)
print C
print "-----"
print D
print " ----- "
print E
import magellan as mg
A = mg.load_dataset('table_A', 'id')
B = mg.load_dataset('table_B', 'id')
Ap, Bp = mg.down_sample(A, B, 5, 2)
print Ap
print Bp
import sys
import magellan as mg
from magellan.gui.debug_gui_base import vis_debug_dt
sys.path.append('/Users/Pradap/Documents/Research/Python-Package/enrique/')
mg.init_jvm()
A = mg.load_dataset('table_A')
B = mg.load_dataset('table_B')
ab = mg.AttrEquivalenceBlocker()
C = ab.block_tables(A, B, 'zipcode', 'zipcode', ['name'], ['name'])
L = mg.read_csv('label_demo.csv', ltable=A, rtable=B)
feat_table = mg.get_features_for_matching(A, B)
G = mg.extract_feature_vecs(L, feature_table=feat_table, attrs_after='gold')
S = mg.train_test_split(G, 8, 7)
dt = mg.DTMatcher(name='DecisionTree')
dt.fit(table = S['train'], exclude_attrs=['_id', 'ltable.ID', 'rtable.ID', 'gold'], target_attr='gold')
dt.predict(table=S['test'], exclude_attrs=['_id', 'ltable.ID', 'rtable.ID', 'gold'], target_attr='predicted',
append=True)
d = mg.eval_matches(S['test'], 'gold', 'predicted')
vis_debug_dt(dt, d, S['test'], exclude_attrs=['_id', 'ltable.ID', 'rtable.ID', 'gold'], feat_table=feat_table)
print "Hi"
def change_table_contents(self):
d = mg.load_dataset('table_B')
self.model = DataModel(d)
self.view.set_model(self.model)
self.view.paint_gui()
self.setLayout(layout)
# QApplication.setStyle(QStyleFactory.create('motif'))
def change_table_contents(self):
d = mg.load_dataset('table_B')
self.model = DataModel(d)
self.view.set_model(self.model)
self.view.paint_gui()
# model = DataModel(d)
# view = MTableView(model)
# view.show()
import magellan as mg
import sys
app = mg._viewapp
d = mg.load_dataset('table_A')
# w = MWidget(d)
w = MurWidget(d)
w.show()
(app.exec_())
import magellan as mg
A = mg.load_dataset('table_A', key='ID')
A1= A[['ID']]
d = mg.get_attr_types(A1)
print(d)
self.debug_widget = TreeViewWithLabel(self, "Tree details", type="dt")
layout = QtGui.QHBoxLayout()
splitter1 = QtGui.QSplitter(QtCore.Qt.Vertical)
splitter1.addWidget(self.left_tuple_widget)
splitter1.addWidget(self.right_tuple_widget)
splitter2 = QtGui.QSplitter(QtCore.Qt.Horizontal)
splitter2.addWidget(splitter1)
splitter2.addWidget(self.debug_widget)
layout.addWidget(splitter2)
self.setLayout(layout)
import magellan as mg
from collections import OrderedDict
app = mg._viewapp
dataframe = mg.load_dataset('table_A')
b = mg.load_dataset('table_B')
metric_data = OrderedDict()
metric_data['Precision'] = 0.95
metric_data['Recall'] = 0.93
metric_data['F1'] = 0.94
metric_data['Num. False Positives'] = 5
metric_data['Num. False Negatives'] = 6
m = MainWindowManager(metric_data, dataframe, b)
m.show()
app.exec_()
# coding=utf-8
import magellan as mg
A = mg.load_dataset('table_A', key='ID')
B = mg.load_dataset('table_B', key='ID')
F = mg.get_features_for_blocking(A, B)
print(F)
# self.debug_widget = DictTableViewWithLabel(self, self.debug_result, 'Debug Result')
self.debug_widget = TreeViewWithLabel(self, "Tree details", type="dt")
layout = QtGui.QHBoxLayout()
splitter1 = QtGui.QSplitter(QtCore.Qt.Vertical)
splitter1.addWidget(self.left_tuple_widget)
splitter1.addWidget(self.right_tuple_widget)
splitter2 = QtGui.QSplitter(QtCore.Qt.Horizontal)
splitter2.addWidget(splitter1)
splitter2.addWidget(self.debug_widget)
layout.addWidget(splitter2)
self.setLayout(layout)
import magellan as mg
from collections import OrderedDict
app = mg._viewapp
dataframe = mg.load_dataset('table_A')
b = mg.load_dataset('table_B')
metric_data = OrderedDict()
metric_data['Precision'] = 0.95
metric_data['Recall'] = 0.93
metric_data['F1'] = 0.94
metric_data['Num. False Positives'] = 5
metric_data['Num. False Negatives'] = 6
m = MainWindowManager(metric_data, dataframe, b)
m.show()
app.exec_()