def test_clear_data_home():
resource = REMOTE_DATASETS["test_remote"]
assert not os.path.exists(resource.filename)
load_arviz_data("test_remote")
assert os.path.exists(resource.filename)
clear_data_home(data_home=os.path.dirname(resource.filename))
assert not os.path.exists(resource.filename)
def test_multi_model():
models = {
"centered": az.load_arviz_data("centered_eight"),
"noncentered": az.load_arviz_data("non_centered_eight"),
}
multi_arviz_to_json(models, "multimodel.zip")
# verify zip file is good
with open("multimodel.zip", "rb") as f:
z = zipfile.ZipFile(f)
elements = z.namelist()
for key in models:
npz_name = key + ".npz"
assert npz_name in elements
z.extract(npz_name)
check_zip(npz_name)
def test_id_conversion_args(self):
stored = load_arviz_data("centered_eight")
IVIES = [
"Yale", "Harvard", "MIT", "Princeton", "Cornell", "Dartmouth",
"Columbia", "Brown"
]
# test dictionary argument...
# I reverse engineered a dictionary out of the centered_eight
# data. That's what this block of code does.
d = stored.posterior.to_dict()
d = d["data_vars"]
test_dict = {} # type: Dict[str, np.ndarray]
for var_name in d:
data = d[var_name]["data"]
# this is a list of chains that is a list of samples...
chain_arrs = []
for chain in data: # list of samples
chain_arrs.append(np.array(chain))
data_arr = np.stack(chain_arrs)
test_dict[var_name] = data_arr
inference_data = convert_to_inference_data(test_dict,
dims={"theta": ["Ivies"]},
coords={"Ivies": IVIES})
assert isinstance(inference_data, InferenceData)
assert set(
inference_data.posterior.coords["Ivies"].values) == set(IVIES)
assert inference_data.posterior["theta"].dims == ("chain", "draw",
"Ivies")
def test_arviz_to_json():
import arviz as az
data = az.load_arviz_data("centered_eight")
arviz_to_json(data, "centered_eight.npz")
check_zip("centered_eight.npz")
# check that we can write to a file descriptor as well as a filename
with open("centered_eight_as_f.npz", "wb") as f:
arviz_to_json(data, f)
check_zip("centered_eight_as_f.npz")
def test_load_local_arviz_data():
inference_data = load_arviz_data("centered_eight")
assert isinstance(inference_data, InferenceData)
assert set(inference_data.observed_data.obs.coords["school"].values) == {
"Hotchkiss",
"Mt. Hermon",
"Choate",
"Deerfield",
"Phillips Andover",
"St. Paul's",
"Lawrenceville",
"Phillips Exeter",
}
assert inference_data.posterior["theta"].dims == ("chain", "draw", "school")
def test_dataset_conversion_idempotent(self):
inference_data = load_arviz_data("centered_eight")
data_set = convert_to_dataset(inference_data.posterior)
assert isinstance(data_set, xr.Dataset)
assert set(data_set.coords["school"].values) == {
"Hotchkiss",
"Mt. Hermon",
"Choate",
"Deerfield",
"Phillips Andover",
"St. Paul's",
"Lawrenceville",
"Phillips Exeter",
}
assert data_set["theta"].dims == ("chain", "draw", "school")
def test_id_conversion_idempotent(self):
stored = load_arviz_data("centered_eight")
inference_data = convert_to_inference_data(stored)
assert isinstance(inference_data, InferenceData)
assert set(inference_data.observed_data.obs.coords["school"].values) == {
"Hotchkiss",
"Mt. Hermon",
"Choate",
"Deerfield",
"Phillips Andover",
"St. Paul's",
"Lawrenceville",
"Phillips Exeter",
}
assert inference_data.posterior["theta"].dims == ("chain", "draw", "school")
def test_sel_method_chain_prior():
idata = load_arviz_data("centered_eight")
original_groups = getattr(idata, "_groups")
idata_subset = idata.sel(inplace=False, chain_prior=False, chain=[0, 1, 3])
groups = getattr(idata_subset, "_groups")
assert np.all(np.isin(groups, original_groups))
for group in groups:
dataset_subset = getattr(idata_subset, group)
dataset = getattr(idata, group)
if "chain" in dataset.dims:
assert "chain" in dataset_subset.dims
if "prior" not in group:
assert np.all(dataset_subset.chain.values == np.array([0, 1, 3]))
else:
assert "chain" not in dataset_subset.dims
with pytest.raises(KeyError):
idata.sel(inplace=False, chain_prior=True, chain=[0, 1, 3])
def test_repr_html(self):
"""Test if the function _repr_html is generating html."""
idata = load_arviz_data("centered_eight")
display_style = OPTIONS["display_style"]
xr.set_options(display_style="html")
html = idata._repr_html_() # pylint: disable=protected-access
assert html is not None
assert "<div" in html
for group in idata._groups: # pylint: disable=protected-access
assert group in html
xr_data = getattr(idata, group)
for item, _ in xr_data.items():
assert item in html
specific_style = ".xr-wrap{width:700px!important;}"
assert specific_style in html
xr.set_options(display_style="text")
html = idata._repr_html_() # pylint: disable=protected-access
assert escape(idata.__repr__()) in html
xr.set_options(display_style=display_style)
def test_map(self, use):
idata = load_arviz_data("centered_eight")
args = []
kwargs = {}
if use is None:
fun = lambda x: x + 3
elif use == "args":
fun = lambda x, a: x + a
args = [3]
else:
fun = lambda x, a: x + a
kwargs = {"a": 3}
groups = ("observed_data", "posterior_predictive")
idata_map = idata.map(fun, groups, args=args, **kwargs)
groups_map = idata_map._groups # pylint: disable=protected-access
assert groups_map == idata._groups # pylint: disable=protected-access
assert np.allclose(idata_map.observed_data.obs,
fun(idata.observed_data.obs, *args, **kwargs))
assert np.allclose(
idata_map.posterior_predictive.obs,
fun(idata.posterior_predictive.obs, *args, **kwargs))
assert np.allclose(idata_map.posterior.mu, idata.posterior.mu)
def test_load_remote_arviz_data():
assert load_arviz_data("test_remote")
"""
ESS Local Plot
==============
_thumb: .7, .5
"""
import arviz as az
az.style.use("arviz-darkgrid")
idata = az.load_arviz_data("centered_eight")
az.plot_ess(idata, var_names=["mu"], kind="local", marker="_", ms=20, mew=2)
"""
Joint Plot
==========
_thumb: .5, .8
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
data = az.load_arviz_data("non_centered_eight")
az.plot_joint(
data,
var_names=["theta"],
coords={"school": ["Choate", "Phillips Andover"]},
kind="hexbin",
figsize=(10, 10),
)
plt.show()
"""
Autocorrelation Plot
====================
_thumb: .8, .8
"""
import arviz as az
az.style.use('arviz-darkgrid')
data = az.load_arviz_data('centered_eight')
az.plot_autocorr(data, var_names=('tau', 'mu'))
"""
Violinplot
==========
_thumb: .2, .8
"""
import arviz as az
az.style.use('arviz-darkgrid')
non_centered = az.load_arviz_data('non_centered_eight')
az.plot_violin(non_centered, var_names=["mu", "tau"], textsize=8)
def test_waic():
"""Test widely available information criterion calculation"""
centered = load_arviz_data('centered_eight')
waic(centered)
def test_bad_checksum():
with pytest.raises(IOError):
load_arviz_data("bad_checksum")
def setup_class(cls):
cls.centered = load_arviz_data('centered_eight')
cls.non_centered = load_arviz_data('non_centered_eight')
"""
Ridgeplot
=========
_thumb: .8, .5
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
rugby_data = az.load_arviz_data("rugby")
axes = az.plot_forest(
rugby_data,
kind="ridgeplot",
var_names=["defs"],
linewidth=4,
combined=True,
ridgeplot_overlap=1.5,
colors="blue",
figsize=(9, 4),
)
axes[0].set_title("Relative defensive strength\nof Six Nation rugby teams")
plt.show()
def test_summary_bad_fmt():
centered = load_arviz_data("centered_eight")
with pytest.raises(TypeError):
summary(centered, fmt="bad_fmt")
def test_summary_fmt(fmt):
centered = load_arviz_data("centered_eight")
summary(centered, fmt=fmt)
def setup_class(cls):
cls.centered = load_arviz_data("centered_eight")
cls.non_centered = load_arviz_data("non_centered_eight")
"""
Quantile MCSE Errobar Plot
==========================
_thumb: .6, .4
"""
import arviz as az
az.style.use('arviz-darkgrid')
data = az.load_arviz_data('radon')
az.plot_mcse(data, var_names=["sigma_a"], color="C4", errorbar=True)
def test_load_local_arviz_data():
assert load_arviz_data("centered_eight")
"""
Bayesian p-value Posterior plot
===============================
_thumb: .6, .5
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
data = az.load_arviz_data("regression1d")
az.plot_bpv(data)
plt.show()
"""
Separationplot
==========
_thumb: .2, .8
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
idata = az.load_arviz_data("classification10d")
az.plot_separation(idata=idata, y="outcome", y_hat="outcome", figsize=(8, 1))
plt.show()
def test_missing_dataset():
with pytest.raises(ValueError):
load_arviz_data("does not exist")
"""
ESS Quantile Plot
=================
_thumb: .4, .5
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
idata = az.load_arviz_data("radon")
az.plot_ess(idata, var_names=["sigma_y"], kind="quantile", color="C4")
plt.show()
def test_summary(include_circ):
centered = load_arviz_data('centered_eight')
summary(centered, include_circ=include_circ)
"""
ELPD Plot
=========
_thumb: .6, .5
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
d1 = az.load_arviz_data("centered_eight")
d2 = az.load_arviz_data("non_centered_eight")
az.plot_elpd({"Centered eight": d1, "Non centered eight": d2}, xlabels=True)
plt.show()
