def test_regression_report():
regressors = RegressorsFactory()
regressors.add_regressor('gb', GradientBoostingRegressor(n_estimators=10))
regressors.add_regressor('rf', RandomForestRegressor())
regressors.add_regressor('ada', AdaBoostRegressor(n_estimators=10))
X, y = generate_regression_sample(1000, 5)
regressors.fit(X, y)
X, y = generate_regression_sample(1000, 5)
test_lds = LabeledDataStorage(X, y, sample_weight=None)
regression_report = regressors.test_on_lds(test_lds)
val = numpy.mean(X['column0'])
_regression_mask_report(regression_report, "column0 > %f" % val, X)
_regression_mask_report(regression_report, lambda x: numpy.array(x['column0']) < val, X)
_regression_mask_report(regression_report, None, X)
def test_factory():
factory = RegressorsFactory()
try:
from rep.estimators.tmva import TMVARegressor
factory.add_regressor(
'tmva',
TMVARegressor(
factory_options="Silent=True:V=False:DrawProgressBar=False"))
except ImportError:
pass
factory.add_regressor('rf', RandomForestRegressor(n_estimators=10))
factory.add_regressor('ada', AdaBoostRegressor(n_estimators=20))
X, y, sample_weight = generate_classification_data()
assert factory == factory.fit(X,
y,
sample_weight=sample_weight,
features=list(X.columns))
values = factory.predict(X)
for cl in factory.values():
assert list(cl.features) == list(X.columns)
for key, val in values.items():
score = mean_squared_error(y, val)
print(score)
assert score < 0.2
for key, iterator in factory.staged_predict(X).items():
assert key != 'tmva', 'tmva does not support staged pp'
for p in iterator:
assert p.shape == (len(X), )
# checking that last iteration coincides with previous
assert numpy.all(p == values[key])
# testing picklability
dump_string = cPickle.dumps(factory)
clf_loaded = cPickle.loads(dump_string)
assert type(factory) == type(clf_loaded)
probs1 = factory.predict(X)
probs2 = clf_loaded.predict(X)
for key, val in probs1.items():
assert numpy.all(val == probs2[key]), 'something strange was loaded'
report = RegressionReport({'rf': factory['rf']},
LabeledDataStorage(X, y, sample_weight))
report.feature_importance_shuffling(mean_squared_mod).plot(new_plot=True,
figsize=(18, 3))
report = factory.test_on_lds(LabeledDataStorage(X, y, sample_weight))
report = factory.test_on(X, y, sample_weight=sample_weight)
report.feature_importance()
report.features_correlation_matrix()
report.predictions_scatter()
val = numpy.mean(X['column0'])
report_mask(report, "column0 > %f" % val, X)
report_mask(report, lambda x: numpy.array(x['column0']) < val, X)
report_mask(report, None, X)
def test_factory():
factory = RegressorsFactory()
try:
from rep.estimators.tmva import TMVARegressor
factory.add_regressor('tmva', TMVARegressor())
except ImportError:
pass
factory.add_regressor('rf', RandomForestRegressor(n_estimators=10))
factory.add_regressor('ada', AdaBoostRegressor(n_estimators=20))
X, y, sample_weight = generate_classification_data()
assert factory == factory.fit(X, y, sample_weight=sample_weight, features=list(X.columns))
values = factory.predict(X)
for cl in factory.values():
assert list(cl.features) == list(X.columns)
for key, val in values.items():
score = mean_squared_error(y, val)
print(score)
assert score < 0.2
for key, iterator in factory.staged_predict(X).items():
assert key != 'tmva', 'tmva does not support staged pp'
for p in iterator:
assert p.shape == (len(X), )
# checking that last iteration coincides with previous
assert numpy.all(p == values[key])
# testing picklability
dump_string = cPickle.dumps(factory)
clf_loaded = cPickle.loads(dump_string)
assert type(factory) == type(clf_loaded)
probs1 = factory.predict(X)
probs2 = clf_loaded.predict(X)
for key, val in probs1.items():
assert numpy.all(val == probs2[key]), 'something strange was loaded'
report = RegressionReport({'rf': factory['rf']}, LabeledDataStorage(X, y, sample_weight))
report.feature_importance_shuffling(mean_squared_mod).plot(new_plot=True, figsize=(18, 3))
report = factory.test_on_lds(LabeledDataStorage(X, y, sample_weight))
report = factory.test_on(X, y, sample_weight=sample_weight)
report.feature_importance()
report.features_correlation_matrix()
report.predictions_scatter()
val = numpy.mean(X['column0'])
report_mask(report, "column0 > %f" % val, X)
report_mask(report, lambda x: numpy.array(x['column0']) < val, X)
report_mask(report, None, X)
def test_regression_report():
regressors = RegressorsFactory()
regressors.add_regressor('gb', GradientBoostingRegressor(n_estimators=10))
regressors.add_regressor('rf', RandomForestRegressor())
regressors.add_regressor('ada', AdaBoostRegressor(n_estimators=10))
X, y = generate_regression_sample(1000, 5)
regressors.fit(X, y)
X, y = generate_regression_sample(1000, 5)
test_lds = LabeledDataStorage(X, y, sample_weight=None)
regression_report = regressors.test_on_lds(test_lds)
val = numpy.mean(X['column0'])
_regression_mask_report(regression_report, "column0 > %f" % val, X)
_regression_mask_report(regression_report,
lambda x: numpy.array(x['column0']) < val, X)
_regression_mask_report(regression_report, None, X)