class TestForwardPasser(object):
def __init__(self):
numpy.random.seed(0)
self.basis = Basis(10)
constant = ConstantBasisFunction()
self.basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
self.basis.append(bf1)
self.basis.append(bf2)
self.basis.append(bf3)
self.X = numpy.random.normal(size=(100, 10))
self.B = numpy.empty(shape=(100, 4), dtype=numpy.float64)
self.basis.transform(self.X, self.B)
self.beta = numpy.random.normal(size=4)
self.y = numpy.empty(shape=100, dtype=numpy.float64)
self.y[:] = numpy.dot(
self.B, self.beta) + numpy.random.normal(size=100)
self.forwardPasser = ForwardPasser(
self.X, self.y, numpy.ones(self.y.shape), max_terms=1000, penalty=1)
def test_orthonormal_update(self):
numpy.set_printoptions(precision=4)
m, n = self.X.shape
B_orth = self.forwardPasser.get_B_orth()
v = numpy.random.normal(size=m)
for i in range(1, 10):
v_ = numpy.random.normal(size=m)
B_orth[:, i] = 10 * v_ + v
v = v_
self.forwardPasser.orthonormal_update(i)
B_orth_dot_B_orth_T = numpy.dot(B_orth[:, 0:i + 1].transpose(),
B_orth[:, 0:i + 1])
assert_true(
numpy.max(numpy.abs(
B_orth_dot_B_orth_T - numpy.eye(i + 1))
) < .0000001
)
def test_run(self):
self.forwardPasser.run()
res = str(self.forwardPasser.get_basis()) + \
'\n' + str(self.forwardPasser.trace())
filename = os.path.join(os.path.dirname(__file__),
'forward_regress.txt')
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
regenerate_target_files = False
numpy.random.seed(1)
basis = Basis(10)
constant = ConstantBasisFunction()
basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
basis.append(bf1)
basis.append(bf2)
basis.append(bf3)
X = numpy.random.normal(size=(1000, 10))
missing = numpy.zeros_like(X, dtype=BOOL)
B = numpy.empty(shape=(1000, 4), dtype=numpy.float64)
basis.transform(X, missing, B)
beta = numpy.random.normal(size=4)
y = numpy.empty(shape=1000, dtype=numpy.float64)
y[:] = numpy.dot(B, beta) + numpy.random.normal(size=1000)
default_params = {"penalty": 1}
@if_platform_not_win_32
@if_sklearn_version_greater_than_or_equal_to('0.17.2')
def test_check_estimator():
numpy.random.seed(0)
import sklearn.utils.estimator_checks
sklearn.utils.estimator_checks.MULTI_OUTPUT.append('Earth')
sklearn.utils.estimator_checks.check_estimator(Earth)
numpy.random.seed(0)
basis = Basis(10)
constant = ConstantBasisFunction()
basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
basis.append(bf1)
basis.append(bf2)
basis.append(bf3)
X = numpy.random.normal(size=(100, 10))
missing = numpy.zeros_like(X, dtype=BOOL)
B = numpy.empty(shape=(100, 4), dtype=numpy.float64)
basis.transform(X, missing, B)
beta = numpy.random.normal(size=4)
y = numpy.empty(shape=100, dtype=numpy.float64)
y[:] = numpy.dot(B, beta) + numpy.random.normal(size=100)
default_params = {"penalty": 1}
@if_sklearn_version_greater_than_or_equal_to('0.17.2')
def test_check_estimator():
import sklearn.utils.estimator_checks
sklearn.utils.estimator_checks.MULTI_OUTPUT.append('Earth')
sklearn.utils.estimator_checks.check_estimator(Earth)
def test_get_params():
assert_equal(
Earth().get_params(), {'penalty': None, 'min_search_points': None,
'endspan_alpha': None, 'check_every': None,
assert_list_almost_equal_value)
numpy.random.seed(0)
basis = Basis(10)
constant = ConstantBasisFunction()
basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
basis.append(bf1)
basis.append(bf2)
basis.append(bf3)
X = numpy.random.normal(size=(100, 10))
B = numpy.empty(shape=(100, 4), dtype=numpy.float64)
basis.transform(X, B)
beta = numpy.random.normal(size=4)
y = numpy.empty(shape=100, dtype=numpy.float64)
y[:] = numpy.dot(B, beta) + numpy.random.normal(size=100)
default_params = {"penalty": 1}
def test_get_params():
assert_equal(
Earth().get_params(), {
'penalty': None,
'min_search_points': None,
'endspan_alpha': None,
'check_every': None,
'max_terms': None,
'max_degree': None,
from pyearth._basis import (Basis, ConstantBasisFunction,
HingeBasisFunction, LinearBasisFunction)
numpy.random.seed(0)
basis = Basis(10)
constant = ConstantBasisFunction()
basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
basis.append(bf1)
basis.append(bf2)
basis.append(bf3)
X = numpy.random.normal(size=(100, 10))
B = numpy.empty(shape=(100, 4), dtype=numpy.float64)
basis.transform(X, B)
beta = numpy.random.normal(size=4)
y = numpy.empty(shape=100, dtype=numpy.float64)
y[:] = numpy.dot(B, beta) + numpy.random.normal(size=100)
def test_orthonormal_update():
forwardPasser = ForwardPasser(X, y, numpy.ones(y.shape),
max_terms=1000, penalty=1)
numpy.set_printoptions(precision=4)
m, n = X.shape
B_orth = forwardPasser.get_B_orth()
v = numpy.random.normal(size=m)
for i in range(1, 10):
class TestEarth(object):
def __init__(self):
numpy.random.seed(0)
self.basis = Basis(10)
constant = ConstantBasisFunction()
self.basis.append(constant)
bf1 = HingeBasisFunction(constant, 0.1, 10, 1, False, 'x1')
bf2 = HingeBasisFunction(constant, 0.1, 10, 1, True, 'x1')
bf3 = LinearBasisFunction(bf1, 2, 'x2')
self.basis.append(bf1)
self.basis.append(bf2)
self.basis.append(bf3)
self.X = numpy.random.normal(size=(100, 10))
self.B = numpy.empty(shape=(100, 4), dtype=numpy.float64)
self.basis.transform(self.X, self.B)
self.beta = numpy.random.normal(size=4)
self.y = numpy.empty(shape=100, dtype=numpy.float64)
self.y[:] = numpy.dot(
self.B, self.beta) + numpy.random.normal(size=100)
self.earth = Earth(penalty=1)
def test_get_params(self):
assert_equal(
Earth().get_params(), {'penalty': None, 'min_search_points': None,
'endspan_alpha': None, 'check_every': None,
'max_terms': None, 'max_degree': None,
'minspan_alpha': None, 'thresh': None,
'minspan': None, 'endspan': None,
'allow_linear': None, 'smooth': None})
assert_equal(
Earth(
max_degree=3).get_params(), {'penalty': None,
'min_search_points': None,
'endspan_alpha': None,
'check_every': None,
'max_terms': None, 'max_degree': 3,
'minspan_alpha': None,
'thresh': None, 'minspan': None,
'endspan': None,
'allow_linear': None,
'smooth': None})
@if_statsmodels
def test_linear_fit(self):
from statsmodels.regression.linear_model import GLS, OLS
self.earth.fit(self.X, self.y)
self.earth._Earth__linear_fit(self.X, self.y)
soln = OLS(self.y, self.earth.transform(self.X)).fit().params
assert_almost_equal(numpy.mean((self.earth.coef_ - soln) ** 2), 0.0)
sample_weight = 1.0 / (numpy.random.normal(size=self.y.shape) ** 2)
self.earth.fit(self.X, self.y)
self.earth._Earth__linear_fit(self.X, self.y, sample_weight)
soln = GLS(self.y, self.earth.transform(
self.X), 1.0 / sample_weight).fit().params
assert_almost_equal(numpy.mean((self.earth.coef_ - soln) ** 2), 0.0)
def test_sample_weight(self):
group = numpy.random.binomial(1, .5, size=1000) == 1
sample_weight = 1 / (group * 100 + 1.0)
x = numpy.random.uniform(-10, 10, size=1000)
y = numpy.abs(x)
y[group] = numpy.abs(x[group] - 5)
y += numpy.random.normal(0, 1, size=1000)
model = Earth().fit(x, y, sample_weight=sample_weight)
# Check that the model fits better for the more heavily weighted group
assert_true(model.score(x[group], y[group]) < model.score(
x[numpy.logical_not(group)], y[numpy.logical_not(group)]))
# Make sure that the score function gives the same answer as the trace
pruning_trace = model.pruning_trace()
rsq_trace = pruning_trace.rsq(model.pruning_trace().get_selected())
assert_almost_equal(model.score(x, y, sample_weight=sample_weight),
rsq_trace)
# Uncomment below to see what this test situation looks like
# from matplotlib import pyplot
# print model.summary()
# print model.score(x,y,sample_weight = sample_weight)
# pyplot.figure()
# pyplot.plot(x,y,'b.')
# pyplot.plot(x,model.predict(x),'r.')
# pyplot.show()
def test_fit(self):
self.earth.fit(self.X, self.y)
res = str(self.earth.trace()) + '\n' + self.earth.summary()
# fl.write(res)
filename = os.path.join(os.path.dirname(__file__),
'earth_regress.txt')
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
def test_smooth(self):
model = Earth(penalty=1, smooth=True)
model.fit(self.X, self.y)
res = str(model.trace()) + '\n' + model.summary()
filename = os.path.join(os.path.dirname(__file__),
'earth_regress_smooth.txt')
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
def test_linvars(self):
self.earth.fit(self.X, self.y, linvars=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
res = str(self.earth.trace()) + '\n' + self.earth.summary()
filename = os.path.join(os.path.dirname(__file__),
'earth_linvars_regress.txt')
with open(filename, 'r') as fl:
prev = fl.read()
assert_equal(res, prev)
def test_score(self):
model = self.earth.fit(self.X, self.y)
record = model.pruning_trace()
rsq = record.rsq(record.get_selected())
assert_almost_equal(rsq, model.score(self.X, self.y))
@if_pandas
@if_environ_has('test_pathological_cases')
def test_pathological_cases(self):
import pandas
directory = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'pathological_data')
cases = {'issue_44': {},
'issue_50': {'penalty': 0.5,
'minspan': 1,
'allow_linear': False,
'endspan': 1,
'check_every': 1,
'sample_weight': 'issue_50_weight.csv'}}
for case, settings in cases.iteritems():
data = pandas.read_csv(os.path.join(directory, case + '.csv'))
y = data['y']
del data['y']
X = data
if 'sample_weight' in settings:
filename = os.path.join(directory, settings['sample_weight'])
sample_weight = pandas.read_csv(filename)['sample_weight']
del settings['sample_weight']
else:
sample_weight = None
model = Earth(**settings)
model.fit(X, y, sample_weight=sample_weight)
with open(os.path.join(directory, case + '.txt'), 'r') as infile:
correct = infile.read()
assert_equal(model.summary(), correct)
@if_pandas
def test_pandas_compatibility(self):
import pandas
X = pandas.DataFrame(self.X)
y = pandas.DataFrame(self.y)
colnames = ['xx' + str(i) for i in range(X.shape[1])]
X.columns = colnames
model = self.earth.fit(X, y)
assert_list_equal(
colnames, model.forward_trace()._getstate()['xlabels'])
@if_patsy
@if_pandas
def test_patsy_compatibility(self):
import pandas
import patsy
X = pandas.DataFrame(self.X)
y = pandas.DataFrame(self.y)
colnames = ['xx' + str(i) for i in range(X.shape[1])]
X.columns = colnames
X['y'] = y
y, X = patsy.dmatrices(
'y ~ xx0 + xx1 + xx2 + xx3 + xx4 + xx5 + xx6 + xx7 + xx8 + xx9 - 1',
data=X)
model = self.earth.fit(X, y)
assert_list_equal(
colnames, model.forward_trace()._getstate()['xlabels'])
def test_pickle_compatibility(self):
model = self.earth.fit(self.X, self.y)
model_copy = pickle.loads(pickle.dumps(model))
assert_true(model_copy == model)
assert_true(
numpy.all(model.predict(self.X) == model_copy.predict(self.X)))
assert_true(model.basis_[0] is model.basis_[1]._get_root())
assert_true(model_copy.basis_[0] is model_copy.basis_[1]._get_root())
def test_copy_compatibility(self):
model = self.earth.fit(self.X, self.y)
model_copy = copy.copy(model)
assert_true(model_copy == model)
assert_true(
numpy.all(model.predict(self.X) == model_copy.predict(self.X)))
assert_true(model.basis_[0] is model.basis_[1]._get_root())
assert_true(model_copy.basis_[0] is model_copy.basis_[1]._get_root())
