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Fix penalty for LogisticRegression #403

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merged 3 commits into from
Feb 21, 2024
Merged

Fix penalty for LogisticRegression #403

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2699cfa
Fix penalty for LogisticRegression
raphaelvallat Feb 3, 2024
f6ca844
Fix unit tests
raphaelvallat Feb 21, 2024
b8ab525
remove tests for py3.8
raphaelvallat Feb 21, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/python_tests.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ jobs:
fail-fast: false
matrix:
platform: [ubuntu-latest, macos-latest, windows-latest]
python-version: ["3.8", "3.9", "3.10", "3.11"]
python-version: ["3.9", "3.10", "3.11"]

runs-on: ${{ matrix.platform }}

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2 changes: 1 addition & 1 deletion pingouin/regression.py
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Expand Up @@ -893,7 +893,7 @@ def logistic_regression(
# Updated in Pingouin > 0.3.6 to be consistent with R
kwargs["solver"] = "newton-cg"
if "penalty" not in kwargs:
kwargs["penalty"] = "none"
kwargs["penalty"] = None
lom = LogisticRegression(**kwargs)
lom.fit(X, y)

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37 changes: 19 additions & 18 deletions pingouin/tests/test_regression.py
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Expand Up @@ -261,29 +261,29 @@ def test_logistic_regression(self):
# Together in one cell below
# %%R -i df
# summary(glm(Ybin ~ X, data=df, family=binomial))
assert_equal(np.round(lom["coef"], 4), [1.3191, -0.1995])
assert_equal(np.round(lom["se"], 4), [0.7582, 0.1211])
assert_equal(np.round(lom["z"], 4), [1.7399, -1.6476])
assert_equal(np.round(lom["pval"], 4), [0.0819, 0.0994])
assert_equal(np.round(lom["CI[2.5%]"], 4), [-0.1669, -0.4367])
assert_equal(np.round(lom["CI[97.5%]"], 4), [2.8050, 0.0378])
assert_equal(np.round(lom["coef"], 3), [1.319, -0.199])
assert_equal(np.round(lom["se"], 3), [0.758, 0.121])
assert_equal(np.round(lom["z"], 3), [1.74, -1.647])
assert_equal(np.round(lom["pval"], 3), [0.082, 0.099])
assert_equal(np.round(lom["CI[2.5%]"], 3), [-0.167, -0.437])
assert_equal(np.round(lom["CI[97.5%]"], 3), [2.805, 0.038])

# Multiple predictors
X = df[["X", "M"]].to_numpy()
y = df["Ybin"].to_numpy()
lom = logistic_regression(X, y).round(4) # Pingouin
lom = logistic_regression(X, y).round(3) # Pingouin
# Compare against R
# summary(glm(Ybin ~ X+M, data=df, family=binomial))
assert_equal(lom["coef"].to_numpy(), [1.3275, -0.1960, -0.0060])
assert_equal(lom["se"].to_numpy(), [0.7784, 0.1408, 0.1253])
assert_equal(lom["z"].to_numpy(), [1.7055, -1.3926, -0.0475])
assert_equal(lom["pval"].to_numpy(), [0.0881, 0.1637, 0.9621])
assert_equal(lom["CI[2.5%]"].to_numpy(), [-0.1981, -0.4719, -0.2516])
assert_equal(lom["CI[97.5%]"].to_numpy(), [2.8531, 0.0799, 0.2397])
assert_equal(lom["coef"].to_numpy(), [1.327, -0.196, -0.006])
assert_equal(lom["se"].to_numpy(), [0.778, 0.141, 0.125])
assert_equal(lom["z"].to_numpy(), [1.705, -1.392, -0.048])
assert_equal(lom["pval"].to_numpy(), [0.088, 0.164, 0.962])
assert_equal(lom["CI[2.5%]"].to_numpy(), [-0.198, -0.472, -0.252])
assert_equal(lom["CI[97.5%]"].to_numpy(), [2.853, 0.08, 0.24])

# Test other arguments
c = logistic_regression(df[["X", "M"]], df["Ybin"], coef_only=True)
assert_equal(np.round(c, 4), [1.3275, -0.1960, -0.0060])
assert_equal(np.round(c, 3), [1.327, -0.196, -0.006])

# With missing values
logistic_regression(df_nan[["X", "M"]], df_nan["Ybin"], remove_na=True)
Expand Down Expand Up @@ -353,11 +353,12 @@ def test_logistic_regression(self):
X = data_dum[["body_mass_kg", "species_Chinstrap", "species_Gentoo"]]
y = data_dum["male"]
lom = logistic_regression(X, y, as_dataframe=False)
assert_equal(np.round(lom["coef"], 7), [-27.1318593, 7.3728436, -0.2559251, -10.1778083])
assert_equal(np.round(lom["se"], 4), [2.9984, 0.8141, 0.4293, 1.1946])
# See https://github.com/raphaelvallat/pingouin/pull/403
assert_equal(np.round(lom["coef"], 2), [-27.13, 7.37, -0.26, -10.18])
assert_equal(np.round(lom["se"], 3), [2.998, 0.814, 0.429, 1.195])
assert_equal(np.round(lom["z"], 3), [-9.049, 9.056, -0.596, -8.520])
assert_equal(np.round(lom["CI[2.5%]"], 3), [-33.009, 5.777, -1.097, -12.519])
assert_equal(np.round(lom["CI[97.5%]"], 3), [-21.255, 8.969, 0.586, -7.836])
assert_equal(np.round(lom["CI[2.5%]"], 1), [-33.0, 5.8, -1.1, -12.5])
assert_equal(np.round(lom["CI[97.5%]"], 1), [-21.3, 9.0, 0.6, -7.8])

def test_mediation_analysis(self):
"""Test function mediation_analysis."""
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