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COSMIT don't use docstrings in tests (gp kernel tests) #7399

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Sep 13, 2016
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COSMIT don't use docstrings in tests (gp kernel tests) #7399

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23 changes: 10 additions & 13 deletions sklearn/gaussian_process/tests/test_gpc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,16 +34,15 @@ def f(x):


def test_predict_consistent():
""" Check binary predict decision has also predicted probability above 0.5.
"""
# Check binary predict decision has also predicted probability above 0.5.
for kernel in kernels:
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
assert_array_equal(gpc.predict(X),
gpc.predict_proba(X)[:, 1] >= 0.5)


def test_lml_improving():
""" Test that hyperparameter-tuning improves log-marginal likelihood. """
# Test that hyperparameter-tuning improves log-marginal likelihood.
for kernel in kernels:
if kernel == fixed_kernel:
continue
Expand All @@ -53,15 +52,15 @@ def test_lml_improving():


def test_lml_precomputed():
""" Test that lml of optimized kernel is stored correctly. """
# Test that lml of optimized kernel is stored correctly.
for kernel in kernels:
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
assert_almost_equal(gpc.log_marginal_likelihood(gpc.kernel_.theta),
gpc.log_marginal_likelihood(), 7)


def test_converged_to_local_maximum():
""" Test that we are in local maximum after hyperparameter-optimization."""
# Test that we are in local maximum after hyperparameter-optimization.
for kernel in kernels:
if kernel == fixed_kernel:
continue
Expand All @@ -76,7 +75,7 @@ def test_converged_to_local_maximum():


def test_lml_gradient():
""" Compare analytic and numeric gradient of log marginal likelihood. """
# Compare analytic and numeric gradient of log marginal likelihood.
for kernel in kernels:
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

Expand All @@ -91,10 +90,8 @@ def test_lml_gradient():


def test_random_starts():
"""
Test that an increasing number of random-starts of GP fitting only
increases the log marginal likelihood of the chosen theta.
"""
# Test that an increasing number of random-starts of GP fitting only
# increases the log marginal likelihood of the chosen theta.
n_samples, n_features = 25, 2
np.random.seed(0)
rng = np.random.RandomState(0)
Expand All @@ -115,7 +112,7 @@ def test_random_starts():


def test_custom_optimizer():
""" Test that GPC can use externally defined optimizers. """
# Test that GPC can use externally defined optimizers.
# Define a dummy optimizer that simply tests 50 random hyperparameters
def optimizer(obj_func, initial_theta, bounds):
rng = np.random.RandomState(0)
Expand All @@ -140,7 +137,7 @@ def optimizer(obj_func, initial_theta, bounds):


def test_multi_class():
""" Test GPC for multi-class classification problems. """
# Test GPC for multi-class classification problems.
for kernel in kernels:
gpc = GaussianProcessClassifier(kernel=kernel)
gpc.fit(X, y_mc)
Expand All @@ -153,7 +150,7 @@ def test_multi_class():


def test_multi_class_n_jobs():
""" Test that multi-class GPC produces identical results with n_jobs>1. """
# Test that multi-class GPC produces identical results with n_jobs>1.
for kernel in kernels:
gpc = GaussianProcessClassifier(kernel=kernel)
gpc.fit(X, y_mc)
Expand Down
41 changes: 19 additions & 22 deletions sklearn/gaussian_process/tests/test_gpr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ def f(x):


def test_gpr_interpolation():
"""Test the interpolating property for different kernels."""
# Test the interpolating property for different kernels.
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_pred, y_cov = gpr.predict(X, return_cov=True)
Expand All @@ -46,7 +46,7 @@ def test_gpr_interpolation():


def test_lml_improving():
""" Test that hyperparameter-tuning improves log-marginal likelihood. """
# Test that hyperparameter-tuning improves log-marginal likelihood.
for kernel in kernels:
if kernel == fixed_kernel:
continue
Expand All @@ -56,15 +56,15 @@ def test_lml_improving():


def test_lml_precomputed():
""" Test that lml of optimized kernel is stored correctly. """
# Test that lml of optimized kernel is stored correctly.
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_equal(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood())


def test_converged_to_local_maximum():
""" Test that we are in local maximum after hyperparameter-optimization."""
# Test that we are in local maximum after hyperparameter-optimization.
for kernel in kernels:
if kernel == fixed_kernel:
continue
Expand All @@ -79,7 +79,7 @@ def test_converged_to_local_maximum():


def test_solution_inside_bounds():
""" Test that hyperparameter-optimization remains in bounds"""
# Test that hyperparameter-optimization remains in bounds#
for kernel in kernels:
if kernel == fixed_kernel:
continue
Expand All @@ -95,7 +95,7 @@ def test_solution_inside_bounds():


def test_lml_gradient():
""" Compare analytic and numeric gradient of log marginal likelihood. """
# Compare analytic and numeric gradient of log marginal likelihood.
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)

Expand All @@ -110,7 +110,7 @@ def test_lml_gradient():


def test_prior():
""" Test that GP prior has mean 0 and identical variances."""
# Test that GP prior has mean 0 and identical variances.
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel)

Expand All @@ -125,7 +125,7 @@ def test_prior():


def test_sample_statistics():
""" Test that statistics of samples drawn from GP are correct."""
# Test that statistics of samples drawn from GP are correct.
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)

Expand All @@ -140,14 +140,14 @@ def test_sample_statistics():


def test_no_optimizer():
""" Test that kernel parameters are unmodified when optimizer is None."""
# Test that kernel parameters are unmodified when optimizer is None.
kernel = RBF(1.0)
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None).fit(X, y)
assert_equal(np.exp(gpr.kernel_.theta), 1.0)


def test_predict_cov_vs_std():
""" Test that predicted std.-dev. is consistent with cov's diagonal."""
# Test that predicted std.-dev. is consistent with cov's diagonal.
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_mean, y_cov = gpr.predict(X2, return_cov=True)
Expand All @@ -156,7 +156,7 @@ def test_predict_cov_vs_std():


def test_anisotropic_kernel():
""" Test that GPR can identify meaningful anisotropic length-scales. """
# Test that GPR can identify meaningful anisotropic length-scales.
# We learn a function which varies in one dimension ten-times slower
# than in the other. The corresponding length-scales should differ by at
# least a factor 5
Expand All @@ -171,10 +171,8 @@ def test_anisotropic_kernel():


def test_random_starts():
"""
Test that an increasing number of random-starts of GP fitting only
increases the log marginal likelihood of the chosen theta.
"""
# Test that an increasing number of random-starts of GP fitting only
# increases the log marginal likelihood of the chosen theta.
n_samples, n_features = 25, 2
np.random.seed(0)
rng = np.random.RandomState(0)
Expand All @@ -197,11 +195,10 @@ def test_random_starts():


def test_y_normalization():
""" Test normalization of the target values in GP
# Test normalization of the target values in GP

Fitting non-normalizing GP on normalized y and fitting normalizing GP
on unnormalized y should yield identical results
"""
# Fitting non-normalizing GP on normalized y and fitting normalizing GP
# on unnormalized y should yield identical results
y_mean = y.mean(0)
y_norm = y - y_mean
for kernel in kernels:
Expand All @@ -226,7 +223,7 @@ def test_y_normalization():


def test_y_multioutput():
""" Test that GPR can deal with multi-dimensional target values"""
# Test that GPR can deal with multi-dimensional target values
y_2d = np.vstack((y, y * 2)).T

# Test for fixed kernel that first dimension of 2d GP equals the output
Expand Down Expand Up @@ -269,7 +266,7 @@ def test_y_multioutput():


def test_custom_optimizer():
""" Test that GPR can use externally defined optimizers. """
# Test that GPR can use externally defined optimizers.
# Define a dummy optimizer that simply tests 50 random hyperparameters
def optimizer(obj_func, initial_theta, bounds):
rng = np.random.RandomState(0)
Expand All @@ -294,7 +291,7 @@ def optimizer(obj_func, initial_theta, bounds):


def test_duplicate_input():
""" Test GPR can handle two different output-values for the same input. """
# Test GPR can handle two different output-values for the same input.
for kernel in kernels:
gpr_equal_inputs = \
GaussianProcessRegressor(kernel=kernel, alpha=1e-2)
Expand Down
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