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import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal, assert_array_almost_equal
from seaborn._core.groupby import GroupBy
from seaborn._stats.density import KDE, _no_scipy
from seaborn._compat import groupby_apply_include_groups
class TestKDE:
@pytest.fixture
def df(self, rng):
n = 100
return pd.DataFrame(dict(
x=rng.uniform(0, 7, n).round(),
y=rng.normal(size=n),
color=rng.choice(["a", "b", "c"], n),
alpha=rng.choice(["x", "y"], n),
))
def get_groupby(self, df, orient):
cols = [c for c in df if c != orient]
return GroupBy([*cols, "group"])
def integrate(self, y, x):
y = np.asarray(y)
x = np.asarray(x)
dx = np.diff(x)
return (dx * y[:-1] + dx * y[1:]).sum() / 2
@pytest.mark.parametrize("ori", ["x", "y"])
def test_columns(self, df, ori):
df = df[[ori, "alpha"]]
gb = self.get_groupby(df, ori)
res = KDE()(df, gb, ori, {})
other = {"x": "y", "y": "x"}[ori]
expected = [ori, "alpha", "density", other]
assert list(res.columns) == expected
@pytest.mark.parametrize("gridsize", [20, 30, None])
def test_gridsize(self, df, gridsize):
ori = "y"
df = df[[ori]]
gb = self.get_groupby(df, ori)
res = KDE(gridsize=gridsize)(df, gb, ori, {})
if gridsize is None:
assert_array_equal(res[ori], df[ori])
else:
assert len(res) == gridsize
@pytest.mark.parametrize("cut", [1, 2])
def test_cut(self, df, cut):
ori = "y"
df = df[[ori]]
gb = self.get_groupby(df, ori)
res = KDE(cut=cut, bw_method=1)(df, gb, ori, {})
vals = df[ori]
bw = vals.std()
assert res[ori].min() == pytest.approx(vals.min() - bw * cut, abs=1e-2)
assert res[ori].max() == pytest.approx(vals.max() + bw * cut, abs=1e-2)
@pytest.mark.parametrize("common_grid", [True, False])
def test_common_grid(self, df, common_grid):
ori = "y"
df = df[[ori, "alpha"]]
gb = self.get_groupby(df, ori)
res = KDE(common_grid=common_grid)(df, gb, ori, {})
vals = df["alpha"].unique()
a = res.loc[res["alpha"] == vals[0], ori].to_numpy()
b = res.loc[res["alpha"] == vals[1], ori].to_numpy()
if common_grid:
assert_array_equal(a, b)
else:
assert np.not_equal(a, b).all()
@pytest.mark.parametrize("common_norm", [True, False])
def test_common_norm(self, df, common_norm):
ori = "y"
df = df[[ori, "alpha"]]
gb = self.get_groupby(df, ori)
res = KDE(common_norm=common_norm)(df, gb, ori, {})
areas = (
res.groupby("alpha")
.apply(
lambda x: self.integrate(x["density"], x[ori]),
**groupby_apply_include_groups(False),
)
)
if common_norm:
assert areas.sum() == pytest.approx(1, abs=1e-3)
else:
assert_array_almost_equal(areas, [1, 1], decimal=3)
def test_common_norm_variables(self, df):
ori = "y"
df = df[[ori, "alpha", "color"]]
gb = self.get_groupby(df, ori)
res = KDE(common_norm=["alpha"])(df, gb, ori, {})
def integrate_by_color_and_sum(x):
return (
x.groupby("color")
.apply(
lambda y: self.integrate(y["density"], y[ori]),
**groupby_apply_include_groups(False)
)
.sum()
)
areas = (
res
.groupby("alpha")
.apply(integrate_by_color_and_sum, **groupby_apply_include_groups(False))
)
assert_array_almost_equal(areas, [1, 1], decimal=3)
@pytest.mark.parametrize("param", ["norm", "grid"])
def test_common_input_checks(self, df, param):
ori = "y"
df = df[[ori, "alpha"]]
gb = self.get_groupby(df, ori)
msg = rf"Undefined variable\(s\) passed for KDE.common_{param}"
with pytest.warns(UserWarning, match=msg):
KDE(**{f"common_{param}": ["color", "alpha"]})(df, gb, ori, {})
msg = f"KDE.common_{param} must be a boolean or list of strings"
with pytest.raises(TypeError, match=msg):
KDE(**{f"common_{param}": "alpha"})(df, gb, ori, {})
def test_bw_adjust(self, df):
ori = "y"
df = df[[ori]]
gb = self.get_groupby(df, ori)
res1 = KDE(bw_adjust=0.5)(df, gb, ori, {})
res2 = KDE(bw_adjust=2.0)(df, gb, ori, {})
mad1 = res1["density"].diff().abs().mean()
mad2 = res2["density"].diff().abs().mean()
assert mad1 > mad2
def test_bw_method_scalar(self, df):
ori = "y"
df = df[[ori]]
gb = self.get_groupby(df, ori)
res1 = KDE(bw_method=0.5)(df, gb, ori, {})
res2 = KDE(bw_method=2.0)(df, gb, ori, {})
mad1 = res1["density"].diff().abs().mean()
mad2 = res2["density"].diff().abs().mean()
assert mad1 > mad2
@pytest.mark.skipif(_no_scipy, reason="KDE.cumulative requires scipy")
@pytest.mark.parametrize("common_norm", [True, False])
def test_cumulative(self, df, common_norm):
ori = "y"
df = df[[ori, "alpha"]]
gb = self.get_groupby(df, ori)
res = KDE(cumulative=True, common_norm=common_norm)(df, gb, ori, {})
for _, group_res in res.groupby("alpha"):
assert (group_res["density"].diff().dropna() >= 0).all()
if not common_norm:
assert group_res["density"].max() == pytest.approx(1, abs=1e-3)
def test_cumulative_requires_scipy(self):
if _no_scipy:
err = "Cumulative KDE evaluation requires scipy"
with pytest.raises(RuntimeError, match=err):
KDE(cumulative=True)
@pytest.mark.parametrize("vals", [[], [1], [1] * 5, [1929245168.06679] * 18])
def test_singular(self, df, vals):
df1 = pd.DataFrame({"y": vals, "alpha": ["z"] * len(vals)})
gb = self.get_groupby(df1, "y")
res = KDE()(df1, gb, "y", {})
assert res.empty
df2 = pd.concat([df[["y", "alpha"]], df1], ignore_index=True)
gb = self.get_groupby(df2, "y")
res = KDE()(df2, gb, "y", {})
assert set(res["alpha"]) == set(df["alpha"])
@pytest.mark.parametrize("col", ["y", "weight"])
def test_missing(self, df, col):
val, ori = "xy"
df["weight"] = 1
df = df[[ori, "weight"]].astype(float)
df.loc[:4, col] = np.nan
gb = self.get_groupby(df, ori)
res = KDE()(df, gb, ori, {})
assert self.integrate(res[val], res[ori]) == pytest.approx(1, abs=1e-3)
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