def _debug_decisiontree_matcher(dt, t1, t2, feat_table, fv_columns, exclude_attrs,
ensemble_flag=False):
if isinstance(dt, DTMatcher):
clf = dt.clf
else:
clf = dt
if exclude_attrs is None:
feature_names = fv_columns
else:
cols = [c not in exclude_attrs for c in fv_columns]
feature_names = fv_columns[cols]
code = get_code(clf, feature_names, ['False', 'True'])
feat_vals = apply_feat_fns(t1, t2, feat_table)
code = get_dbg_fn(code)
d = {}
d.update(feat_vals)
six.exec_(code, d)
ret_val = d['debug_fn']()
if ensemble_flag is True:
spacer = " "
else:
spacer = ""
if ensemble_flag is True:
p = get_prob(clf, t1, t2, feat_table, feature_names)
print(spacer + "Prob. for non-match : " + str(p[0]))
print(spacer + "Prob for match : " + str(p[1]))
return p
else:
print(spacer + "Match status : " + str(ret_val))
def get_prob(clf, t1, t2, feat_table, feature_names):
feat_values = apply_feat_fns(t1, t2, feat_table)
feat_values = pd.Series(feat_values)
feat_values = feat_values[feature_names]
v = feat_values.values
v = v.reshape(1, -1)
p = clf.predict_proba(v)
return p[0]
def get_prob(clf, t1, t2, feat_table, feature_names):
feat_values = apply_feat_fns(t1, t2, feat_table)
feat_values = pd.Series(feat_values)
feat_values = feat_values[feature_names]
v = feat_values.values
if mg._impute_flag == True:
imp = Imputer(missing_values='NaN', strategy='median', axis=0)
imp.fit(v)
v = imp.transform(v)
p = clf.predict_proba(v)
return p[0]
def get_prob(clf, t1, t2, feat_table, feature_names):
feat_values = apply_feat_fns(t1, t2, feat_table)
feat_values = pd.Series(feat_values)
feat_values = feat_values[feature_names]
v = feat_values.values
if mg._impute_flag == True:
imp = Imputer(missing_values='NaN', strategy='median', axis=0)
imp.fit(v)
v = imp.transform(v)
p = clf.predict_proba(v)
return p[0]
def debug_decisiontree_matcher(dt,
t1,
t2,
feat_table,
fv_columns,
exclude_attrs,
ensemble_flag=False):
if isinstance(dt, DTMatcher):
clf = dt.clf
else:
clf = dt
if exclude_attrs is None:
feature_names = fv_columns
else:
cols = [c not in exclude_attrs for c in fv_columns]
feature_names = fv_columns[cols]
code = get_code(clf, feature_names, ['False', 'True'])
feat_vals = apply_feat_fns(t1, t2, feat_table)
code = get_dbg_fn(code)
# print code
d = {}
d.update(feat_vals)
exec code in d
ret_val = d['debug_fn']()
if ensemble_flag is True:
spacer = " "
else:
spacer = ""
if ensemble_flag is True:
p = get_prob(clf, t1, t2, feat_table, feature_names)
print spacer + "Prob. for non-match : " + str(p[0])
print spacer + "Prob for match : " + str(p[1])
return p
else:
print spacer + "Match status : " + str(ret_val)
def get_feature_vector(t1, t2, feat_table):
fv = apply_feat_fns(t1, t2, feat_table)
return fv
def get_feature_vector(t1, t2, feat_table):
fv = apply_feat_fns(t1, t2, feat_table)
return fv
