def vis_debug_rf(matcher, train, test, exclude_attrs, target_attr):
"""
Visual debugger for random forest matcher
Parameters
----------
matcher : object, RFMatcher object
train : MTable, containing training data with "True" labels
test : MTable, containing test data with "True labels.
The "True" labels are used for evaluation.
exclude_attrs : List, attributes to be excluded from train and test,
for training and testing.
target_attr : String, column name in validation_set containing 'True' labels
"""
assert set(test.columns) == set(train.columns), "The train and test columns are not same"
assert set(train.columns).intersection(exclude_attrs) == set(exclude_attrs), "Some of exclude attrs are not part of" \
"train columns"
# redundant
assert set(test.columns).intersection(exclude_attrs) == set(exclude_attrs), "Some of exclude attrs are not part of" \
"test columns"
# fit using training data
matcher.fit(table=train, exclude_attrs=exclude_attrs, target_attr=target_attr)
predict_attr_name = get_name_for_predict_column(test.columns)
predicted = matcher.predict(table=test, exclude_attrs=exclude_attrs, target_attr=predict_attr_name, append=True,
inplace=False)
eval_summary = mg.eval_matches(predicted, target_attr, predict_attr_name)
metric = get_metric(eval_summary)
fp_dataframe = get_dataframe(predicted, eval_summary['false_pos_ls'])
fn_dataframe = get_dataframe(predicted, eval_summary['false_neg_ls'])
app = mg._viewapp
m = MainWindowManager(matcher, "rf", exclude_attrs, metric, predicted, fp_dataframe, fn_dataframe)
m.show()
app.exec_()
def _vis_debug_dt(matcher, train, test, exclude_attrs, target_attr, show_window=True):
if not isinstance(matcher, DTMatcher):
logger.error('Input matcher is not of type Decision Tree matcher')
raise AssertionError('Input matcher is not of type Decision Tree matcher')
if not isinstance(target_attr, six.string_types):
logger.error('Target attribute is not of type string')
raise AssertionError('Target attribute is not of type string')
if not check_attrs_present(train, exclude_attrs):
logger.error('The exclude attrs are not in train table columns')
raise AssertionError('The exclude attrs are not in the train table columns')
if not check_attrs_present(train, target_attr):
logger.error('The target attr is not in train table columns')
raise AssertionError('The target attr is not in the train table columns')
if not check_attrs_present(test, exclude_attrs):
logger.error('The exclude attrs are not in test table columns')
raise AssertionError('The exclude attrs are not in the test table columns')
if not isinstance(exclude_attrs, list):
exclude_attrs = [exclude_attrs]
exclude_attrs = list_drop_duplicates(exclude_attrs)
if target_attr not in exclude_attrs:
exclude_attrs.append(target_attr)
# fit using training data
matcher.fit(table=train, exclude_attrs=exclude_attrs, target_attr=target_attr)
predict_attr_name = get_name_for_predict_column(test.columns)
# predict using the test data
predicted = matcher.predict(table=test, exclude_attrs=exclude_attrs,
target_attr=predict_attr_name, append=True,
inplace=False)
eval_summary = mg.eval_matches(predicted, target_attr, predict_attr_name)
metric = get_metric(eval_summary)
fp_dataframe = get_dataframe(predicted, eval_summary['false_pos_ls'])
fn_dataframe = get_dataframe(predicted, eval_summary['false_neg_ls'])
app = mg._viewapp
m = MainWindowManager(matcher, "dt", exclude_attrs, metric, predicted, fp_dataframe,
fn_dataframe)
if show_window == True:
m.show()
app.exec_()
def vis_debug_dt(matcher, train, test, exclude_attrs, target_attr):
"""
Visual debugger for decision tree matcher
Parameters
----------
matcher : object, DTMatcher object
train : MTable, containing training data with "True" labels
test : MTable, containing test data with "True labels.
The "True" labels are used for evaluation.
exclude_attrs : List, attributes to be excluded from train and test,
for training and testing.
target_attr : String, column name in validation_set containing 'True' labels
"""
assert set(test.columns) == set(
train.columns), "The train and test columns are not same"
assert set(train.columns).intersection(exclude_attrs) == set(exclude_attrs), "Some of exclude attrs are not part of" \
"train columns"
# redundant
assert set(test.columns).intersection(exclude_attrs) == set(exclude_attrs), "Some of exclude attrs are not part of" \
"test columns"
# fit using training data
matcher.fit(table=train,
exclude_attrs=exclude_attrs,
target_attr=target_attr)
predict_attr_name = get_name_for_predict_column(test.columns)
predicted = matcher.predict(table=test,
exclude_attrs=exclude_attrs,
target_attr=predict_attr_name,
append=True,
inplace=False)
# print predicted
eval_summary = mg.eval_matches(predicted, target_attr, predict_attr_name)
# print eval_summary
metric = get_metric(eval_summary)
fp_dataframe = get_dataframe(predicted, eval_summary['false_pos_ls'])
# print fp_dataframe.dtypes
fn_dataframe = get_dataframe(predicted, eval_summary['false_neg_ls'])
app = mg._viewapp
m = MainWindowManager(matcher, "dt", exclude_attrs, metric, predicted,
fp_dataframe, fn_dataframe)
m.show()
app.exec_()
def vis_debug_rm(matcher, validation_set, target_attr, feature_table):
"""
Visual debugger for boolean rule-based matcher
Parameters
----------
matcher : object, Boolean rule-based matcher object
validation_set : MTable, used to debug
target_attr : String, column name in validation_set containing 'True' labels
feature_table : pandas dataframe, containing feature information
"""
predict_attr_name = get_name_for_predict_column(validation_set.columns)
predicted = matcher.predict(validation_set, predict_attr_name, append=True, inplace=False)
eval_summary = mg.eval_matches(predicted, target_attr, predict_attr_name)
metric = get_metric(eval_summary)
fp_dataframe = get_dataframe(predicted, eval_summary['false_pos_ls'])
fn_dataframe = get_dataframe(predicted, eval_summary['false_neg_ls'])
app = mg._viewapp
m = MainWindowManager(matcher, "rm", feature_table, metric, predicted, fp_dataframe, fn_dataframe)
m.show()
app.exec_()
def _vis_debug_dt(matcher,
train,
test,
exclude_attrs,
target_attr,
show_window=True):
if not isinstance(matcher, DTMatcher):
logger.error('Input matcher is not of type Decision Tree matcher')
raise AssertionError(
'Input matcher is not of type Decision Tree matcher')
if not isinstance(target_attr, six.string_types):
logger.error('Target attribute is not of type string')
raise AssertionError('Target attribute is not of type string')
if not check_attrs_present(train, exclude_attrs):
logger.error('The exclude attrs are not in train table columns')
raise AssertionError(
'The exclude attrs are not in the train table columns')
if not check_attrs_present(train, target_attr):
logger.error('The target attr is not in train table columns')
raise AssertionError(
'The target attr is not in the train table columns')
if not check_attrs_present(test, exclude_attrs):
logger.error('The exclude attrs are not in test table columns')
raise AssertionError(
'The exclude attrs are not in the test table columns')
if not isinstance(exclude_attrs, list):
exclude_attrs = [exclude_attrs]
exclude_attrs = list_drop_duplicates(exclude_attrs)
if target_attr not in exclude_attrs:
exclude_attrs.append(target_attr)
# fit using training data
matcher.fit(table=train,
exclude_attrs=exclude_attrs,
target_attr=target_attr)
predict_attr_name = get_name_for_predict_column(test.columns)
# predict using the test data
predicted = matcher.predict(table=test,
exclude_attrs=exclude_attrs,
target_attr=predict_attr_name,
append=True,
inplace=False)
eval_summary = mg.eval_matches(predicted, target_attr, predict_attr_name)
metric = get_metric(eval_summary)
fp_dataframe = get_dataframe(predicted, eval_summary['false_pos_ls'])
fn_dataframe = get_dataframe(predicted, eval_summary['false_neg_ls'])
app = mg._viewapp
m = MainWindowManager(matcher, "dt", exclude_attrs, metric, predicted,
fp_dataframe, fn_dataframe)
if show_window == True:
m.show()
app.exec_()
