def test_MKDAChi2_fdr(testdata_cbma):
"""Smoke test for MKDAChi2."""
meta = MKDAChi2()
res = meta.fit(testdata_cbma, testdata_cbma)
corr = FDRCorrector(method="bh", alpha=0.001)
cres = corr.transform(res)
assert isinstance(res, nimare.results.MetaResult)
assert isinstance(cres, nimare.results.MetaResult)
def test_mkda_chi2_fdr():
"""
Smoke test for MKDAChi2
"""
meta = mkda.MKDAChi2()
res = meta.fit(pytest.cbma_testdata1, pytest.cbma_testdata1)
corr = FDRCorrector(method='fdr_bh', alpha=0.001)
cres = corr.transform(res)
assert isinstance(res, nimare.base.MetaResult)
assert isinstance(cres, nimare.base.MetaResult)
def test_Fishers(testdata_ibma):
"""
Smoke test for Fisher's.
"""
meta = ibma.Fishers()
res = meta.fit(testdata_ibma)
corr = FDRCorrector(method="indep", alpha=0.001)
cres = corr.transform(res)
assert isinstance(meta.results, nimare.results.MetaResult)
assert isinstance(res, nimare.results.MetaResult)
assert isinstance(cres, nimare.results.MetaResult)
def test_fishers():
"""
Smoke test for Fisher's.
"""
meta = ibma.Fishers()
res = meta.fit(pytest.dset_z)
corr = FDRCorrector(method='indep', alpha=0.001)
cres = corr.transform(res)
assert isinstance(meta.results, nimare.base.MetaResult)
assert isinstance(res, nimare.base.MetaResult)
assert isinstance(cres, nimare.base.MetaResult)
def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=False):
meta = ALE(kernel__fwhm=10)
results = meta.fit(dset)
if correct:
corr = FDRCorrector()
cres = corr.transform(results)
else:
cres = results
# get the z coordinates
if ground_truth_foci:
stat_map_kwargs = {"cut_coords": [c[2] for c in ground_truth_foci]}
else:
stat_map_kwargs = {}
fig, ax = plt.subplots()
display = plot_stat_map(
cres.get_map("z"),
display_mode="z",
draw_cross=False,
cmap="Purples",
threshold=2.3,
symmetric_cbar=False,
figure=fig,
axes=ax,
**stat_map_kwargs,
)
if ground_truth_foci:
# place red dots indicating the ground truth foci
display.add_markers(ground_truth_foci)
if return_cres:
return fig, cres
return fig
def test_ALE_approximate_null_unit(testdata_cbma, tmp_path_factory):
"""Unit test for ALE with approximate null_method."""
tmpdir = tmp_path_factory.mktemp("test_ALE_approximate_null_unit")
out_file = os.path.join(tmpdir, "file.pkl.gz")
meta = ale.ALE(null_method="approximate")
res = meta.fit(testdata_cbma)
assert "stat" in res.maps.keys()
assert "p" in res.maps.keys()
assert "z" in res.maps.keys()
assert isinstance(res, nimare.results.MetaResult)
assert isinstance(res.get_map("z", return_type="image"), nib.Nifti1Image)
assert isinstance(res.get_map("z", return_type="array"), np.ndarray)
res2 = res.copy()
assert res2 != res
assert isinstance(res, nimare.results.MetaResult)
# Test saving/loading
meta.save(out_file, compress=True)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=True)
assert isinstance(meta2, ale.ALE)
with pytest.raises(pickle.UnpicklingError):
ale.ALE.load(out_file, compressed=False)
meta.save(out_file, compress=False)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=False)
assert isinstance(meta2, ale.ALE)
with pytest.raises(OSError):
ale.ALE.load(out_file, compressed=True)
# Test MCC methods
# Monte Carlo FWE
corr = FWECorrector(method="montecarlo", voxel_thresh=0.001, n_iters=5, n_cores=-1)
cres = corr.transform(meta.results)
assert isinstance(cres, nimare.results.MetaResult)
assert "z_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "z_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "logp_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "logp_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert isinstance(
cres.get_map("z_level-cluster_corr-FWE_method-montecarlo", return_type="image"),
nib.Nifti1Image,
)
assert isinstance(
cres.get_map("z_level-cluster_corr-FWE_method-montecarlo", return_type="array"), np.ndarray
)
# Bonferroni FWE
corr = FWECorrector(method="bonferroni")
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="image"), nib.Nifti1Image
)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="array"), np.ndarray
)
# FDR
corr = FDRCorrector(method="indep", alpha=0.05)
cres = corr.transform(meta.results)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FDR_method-indep", return_type="image"), nib.Nifti1Image
)
assert isinstance(cres.get_map("z_corr-FDR_method-indep", return_type="array"), np.ndarray)
meta = Stouffers(resample=True)
results = meta.fit(dset)
print(f"FWECorrector options: {FWECorrector.inspect(results)}")
print(f"FDRCorrector options: {FDRCorrector.inspect(results)}")
###############################################################################
# Note that the FWECorrector does not support a "montecarlo" method for the
# Stouffers Estimator.
# This is because NiMARE does not have a Monte Carlo-based method implemented
# for most IBMA algorithms.
###############################################################################
# Apply the Corrector to the MetaResult
# =============================================================================
corr = FDRCorrector(method="indep", alpha=0.05)
cres = corr.transform(results)
###############################################################################
# Show corrected results
# =============================================================================
fig, axes = plt.subplots(figsize=(8, 6), nrows=2)
plot_stat_map(
cres.get_map("z"),
draw_cross=False,
cmap="RdBu_r",
threshold=0.5,
cut_coords=[0, 0, -8],
figure=fig,
axes=axes[0],
)
def test_ALE_montecarlo_null_unit(testdata_cbma, tmp_path_factory):
"""Unit test for ALE with an montecarlo null_method.
This test is run with low-memory kernel transformation as well.
"""
tmpdir = tmp_path_factory.mktemp("test_ALE_montecarlo_null_unit")
out_file = os.path.join(tmpdir, "file.pkl.gz")
meta = ale.ALE(null_method="montecarlo",
n_iters=10,
kernel__memory_limit="1gb")
res = meta.fit(testdata_cbma)
assert "stat" in res.maps.keys()
assert "p" in res.maps.keys()
assert "z" in res.maps.keys()
assert isinstance(res, nimare.results.MetaResult)
assert isinstance(res.get_map("z", return_type="image"), nib.Nifti1Image)
assert isinstance(res.get_map("z", return_type="array"), np.ndarray)
res2 = res.copy()
assert res2 != res
assert isinstance(res, nimare.results.MetaResult)
# Test saving/loading
meta.save(out_file, compress=True)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=True)
assert isinstance(meta2, ale.ALE)
with pytest.raises(pickle.UnpicklingError):
ale.ALE.load(out_file, compressed=False)
meta.save(out_file, compress=False)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=False)
assert isinstance(meta2, ale.ALE)
with pytest.raises(OSError):
ale.ALE.load(out_file, compressed=True)
# Test MCC methods
# Monte Carlo FWE
corr = FWECorrector(method="montecarlo",
voxel_thresh=0.001,
n_iters=5,
n_cores=-1)
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert "z_desc-size_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "z_desc-mass_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "z_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "logp_desc-size_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "logp_desc-mass_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "logp_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert isinstance(
cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo",
return_type="image"),
nib.Nifti1Image,
)
assert isinstance(
cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo",
return_type="array"),
np.ndarray,
)
assert isinstance(
cres.get_map("z_desc-mass_level-cluster_corr-FWE_method-montecarlo",
return_type="image"),
nib.Nifti1Image,
)
assert isinstance(
cres.get_map("z_desc-mass_level-cluster_corr-FWE_method-montecarlo",
return_type="array"),
np.ndarray,
)
# Check that the updated null distribution is in the corrected MetaResult's Estimator.
assert ("values_desc-mass_level-cluster_corr-fwe_method-montecarlo"
in cres.estimator.null_distributions_.keys())
# The updated null distribution should *not* be in the original Estimator, nor in the
# uncorrected MetaResult's Estimator.
assert ("values_desc-mass_level-cluster_corr-fwe_method-montecarlo"
not in meta.null_distributions_.keys())
assert ("values_desc-mass_level-cluster_corr-fwe_method-montecarlo"
not in res.estimator.null_distributions_.keys())
# Bonferroni FWE
corr = FWECorrector(method="bonferroni")
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="image"),
nib.Nifti1Image)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="array"),
np.ndarray)
# FDR
corr = FDRCorrector(method="indep", alpha=0.05)
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FDR_method-indep", return_type="image"),
nib.Nifti1Image)
assert isinstance(
cres.get_map("z_corr-FDR_method-indep", return_type="array"),
np.ndarray)
##########################################
# multiple comparison correctors (testing)
##########################################
@pytest.fixture(
scope="session",
params=[
pytest.param(FWECorrector(method="bonferroni"), id="fwe_bonferroni"),
pytest.param(
FWECorrector(method="montecarlo",
voxel_thresh=ALPHA,
n_iters=100,
n_cores=N_CORES),
id="fwe_montecarlo",
),
pytest.param(FDRCorrector(method="indep", alpha=ALPHA),
id="fdr_indep"),
pytest.param(FDRCorrector(method="negcorr", alpha=ALPHA),
id="fdr_negcorr"),
],
)
def corr(request):
"""Define multiple comparisons correctors for tests."""
return request.param
##########################################
# multiple comparison correctors (smoke)
##########################################
@pytest.fixture(
scope="session",
figure=fig,
axes=axes[i_ax],
)
axes[i_ax].set_title(title)
from nimare.correct import FDRCorrector
###############################################################################
# Multiple comparisons correction in image-based meta-analyses
# -----------------------------------------------------------------------------
from nimare.meta.ibma import Stouffers
meta = Stouffers(resample=True)
results = meta.fit(dset)
print(FWECorrector.inspect(results))
print(FDRCorrector.inspect(results))
###############################################################################
# Note that the FWECorrector does not support a "montecarlo" method for the
# Stouffers Estimator.
# This is because NiMARE does not have a Monte Carlo-based method implemented
# for most IBMA algorithms.
###############################################################################
# Apply the Corrector to the MetaResult
# =============================================================================
corr = FDRCorrector(method="indep", alpha=0.05)
cres = corr.transform(results)
###############################################################################
# Show corrected results