datasets_path = os.sep.join([mg.get_install_path(), 'datasets', 'test_datasets'])
path_c = os.sep.join([datasets_path, 'C.csv'])
A = mg.load_dataset('table_A', key='ID')
B = mg.load_dataset('table_B', key='ID')
C = mg.read_csv_metadata(path_c, ltable=A, rtable=B)
labels = [0] * 7
labels.extend([1] * 8)
C['labels'] = labels
feature_table = mg.get_features_for_matching(A, B)
feature_vectors = mg.extract_feature_vecs(C, feature_table=feature_table,
attrs_after='labels')
dt = mg.DTMatcher()
dt.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels')
vis_tuple_debug_dt_matcher(dt, feature_vectors.ix[0],
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'])
# feature_table = mg.get_features_for_matching(A, B)
#
# labels = [0]*7
# labels.extend([1]*8)
# C['labels'] = labels
#
# feature_vectors = mg.extract_feature_vecs(C, feature_table=feature_table,
# attrs_after='labels')
#
#
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"
s_prime = mg.extract_feature_vecs(L,
attrs_before=['ltable.name', 'rtable.name'],
feature_table=feat_table,
attrs_after=['gold_label'])
# In[34]:
# display feature vector table
s_prime
# In[35]:
# Fitting/Predicting
# Create a set of matchers
nb = mg.NBMatcher() # naive bayes
dt = mg.DTMatcher() # decision tree
rf = mg.RFMatcher() # random forest
# In[36]:
# Select a matcher using cross validation
m = mg.select_matcher([nb, dt, rf],
x=s_prime[list(feat_table['feature_name'])],
y=s_prime['gold_label'],
k=5)
# In[37]:
# see what was selected and the stats
m