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fix: Deprecate n_alphas across estimators for scikit-learn 1.9 compatibility #1043

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fix: Deprecate n_alphas across estimators for scikit-learn 1.9 compatibility #1043

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13 changes: 13 additions & 0 deletions econml/dml/dml.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1134,8 +1134,10 @@ def __init__(self, *,
model_y='auto', model_t='auto',
alpha='auto',
n_alphas=100,
alphas=None,
alpha_cov='auto',
n_alphas_cov=10,
alphas_cov=None,
max_iter=1000,
tol=1e-4,
n_jobs=None,
Expand All @@ -1153,10 +1155,19 @@ def __init__(self, *,
allow_missing=False,
use_ray=False,
ray_remote_func_options=None):
import warnings
if n_alphas != 100:
warnings.warn("The n_alphas parameter is deprecated and will be removed in a future release. "
"Use the alphas parameter instead.", FutureWarning)
if n_alphas_cov != 10:
warnings.warn("The n_alphas_cov parameter is deprecated and will be removed in a future release. "
"Use the alphas_cov parameter instead.", FutureWarning)
self.alpha = alpha
self.n_alphas = n_alphas
self.alphas = alphas
self.alpha_cov = alpha_cov
self.n_alphas_cov = n_alphas_cov
self.alphas_cov = alphas_cov
self.max_iter = max_iter
self.tol = tol
self.n_jobs = n_jobs
Expand Down Expand Up @@ -1185,8 +1196,10 @@ def _gen_allowed_missing_vars(self):
def _gen_model_final(self):
return MultiOutputDebiasedLasso(alpha=self.alpha,
n_alphas=self.n_alphas,
alphas=self.alphas,
alpha_cov=self.alpha_cov,
n_alphas_cov=self.n_alphas_cov,
alphas_cov=self.alphas_cov,
fit_intercept=False,
max_iter=self.max_iter,
tol=self.tol,
Expand Down
13 changes: 13 additions & 0 deletions econml/dr/_drlearner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1616,8 +1616,10 @@ def __init__(self, *,
discrete_outcome=False,
alpha='auto',
n_alphas=100,
alphas=None,
alpha_cov='auto',
n_alphas_cov=10,
alphas_cov=None,
max_iter=1000,
tol=1e-4,
n_jobs=None,
Expand All @@ -1632,11 +1634,20 @@ def __init__(self, *,
use_ray=False,
ray_remote_func_options=None):

import warnings
if n_alphas != 100:
warnings.warn("The n_alphas parameter is deprecated and will be removed in a future release. "
"Use the alphas parameter instead.", FutureWarning)
if n_alphas_cov != 10:
warnings.warn("The n_alphas_cov parameter is deprecated and will be removed in a future release. "
"Use the alphas_cov parameter instead.", FutureWarning)
self.fit_cate_intercept = fit_cate_intercept
self.alpha = alpha
self.n_alphas = n_alphas
self.alphas = alphas
self.alpha_cov = alpha_cov
self.n_alphas_cov = n_alphas_cov
self.alphas_cov = alphas_cov
self.max_iter = max_iter
self.tol = tol
self.n_jobs = n_jobs
Expand All @@ -1663,8 +1674,10 @@ def _gen_allowed_missing_vars(self):
def _gen_model_final(self):
return DebiasedLasso(alpha=self.alpha,
n_alphas=self.n_alphas,
alphas=self.alphas,
alpha_cov=self.alpha_cov,
n_alphas_cov=self.n_alphas_cov,
alphas_cov=self.alphas_cov,
fit_intercept=self.fit_cate_intercept,
max_iter=self.max_iter,
tol=self.tol,
Expand Down
13 changes: 13 additions & 0 deletions econml/iv/dr/_dr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1790,8 +1790,10 @@ def __init__(self, *,
fit_cate_intercept=True,
alpha='auto',
n_alphas=100,
alphas=None,
alpha_cov='auto',
n_alphas_cov=10,
alphas_cov=None,
max_iter=1000,
tol=1e-4,
n_jobs=None,
Expand All @@ -1809,10 +1811,19 @@ def __init__(self, *,
allow_missing=False,
use_ray=False,
ray_remote_func_options=None):
import warnings
if n_alphas != 100:
warnings.warn("The n_alphas parameter is deprecated and will be removed in a future release. "
"Use the alphas parameter instead.", FutureWarning)
if n_alphas_cov != 10:
warnings.warn("The n_alphas_cov parameter is deprecated and will be removed in a future release. "
"Use the alphas_cov parameter instead.", FutureWarning)
self.alpha = alpha
self.n_alphas = n_alphas
self.alphas = alphas
self.alpha_cov = alpha_cov
self.n_alphas_cov = n_alphas_cov
self.alphas_cov = alphas_cov
self.max_iter = max_iter
self.tol = tol
self.n_jobs = n_jobs
Expand Down Expand Up @@ -1849,8 +1860,10 @@ def __init__(self, *,
def _gen_model_final(self):
return DebiasedLasso(alpha=self.alpha,
n_alphas=self.n_alphas,
alphas=self.alphas,
alpha_cov=self.alpha_cov,
n_alphas_cov=self.n_alphas_cov,
alphas_cov=self.alphas_cov,
fit_intercept=False,
max_iter=self.max_iter,
tol=self.tol,
Expand Down
26 changes: 19 additions & 7 deletions econml/sklearn_extensions/linear_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -594,13 +594,14 @@ def fit(self, X, y, sample_weight=None):
return self


def _get_theta_coefs_and_tau_sq(i, X, sample_weight, alpha_cov, n_alphas_cov, max_iter, tol, random_state):
def _get_theta_coefs_and_tau_sq(i, X, sample_weight, alpha_cov, n_alphas_cov, max_iter, tol, random_state,
alphas_cov=None):
n_samples, n_features = X.shape
y = X[:, i]
X_reduced = X[:, list(range(i)) + list(range(i + 1, n_features))]
# Call weighted lasso on reduced design matrix
if alpha_cov == 'auto':
local_wlasso = WeightedLassoCV(cv=3, n_alphas=n_alphas_cov,
local_wlasso = WeightedLassoCV(cv=3, n_alphas=n_alphas_cov, alphas=alphas_cov,
fit_intercept=False,
max_iter=max_iter,
tol=tol, n_jobs=1,
Expand Down Expand Up @@ -726,11 +727,20 @@ class DebiasedLasso(WeightedLasso):
def __init__(self, alpha='auto', n_alphas=100, alpha_cov='auto', n_alphas_cov=10,
fit_intercept=True, precompute=False, copy_X=True, max_iter=1000,
tol=1e-4, warm_start=False,
random_state=None, selection='cyclic', n_jobs=None):
random_state=None, selection='cyclic', n_jobs=None, *, alphas=None, alphas_cov=None):
import warnings
if n_alphas != 100:
warnings.warn("The n_alphas parameter is deprecated and will be removed in a future release. "
"Use the alphas parameter instead.", FutureWarning)
if n_alphas_cov != 10:
warnings.warn("The n_alphas_cov parameter is deprecated and will be removed in a future release. "
"Use the alphas_cov parameter instead.", FutureWarning)
self.n_jobs = n_jobs
self.n_alphas = n_alphas
self.alphas = alphas
self.alpha_cov = alpha_cov
self.n_alphas_cov = n_alphas_cov
self.alphas_cov = alphas_cov
super().__init__(
alpha=alpha, fit_intercept=fit_intercept,
precompute=precompute, copy_X=copy_X,
Expand Down Expand Up @@ -929,7 +939,8 @@ def _get_coef_correction(self, X, y, y_pred, sample_weight, theta_hat):

def _get_optimal_alpha(self, X, y, sample_weight):
# To be done once per target. Assumes y can be flattened.
cv_estimator = WeightedLassoCV(cv=5, n_alphas=self.n_alphas, fit_intercept=self.fit_intercept,
cv_estimator = WeightedLassoCV(cv=5, n_alphas=self.n_alphas, alphas=self.alphas,
fit_intercept=self.fit_intercept,
precompute=self.precompute, copy_X=True,
max_iter=self.max_iter, tol=self.tol,
random_state=self.random_state,
Expand All @@ -950,7 +961,7 @@ def _get_theta_hat(self, X, sample_weight):
results = Parallel(n_jobs=self.n_jobs)(
delayed(_get_theta_coefs_and_tau_sq)(i, X, sample_weight,
self.alpha_cov, self.n_alphas_cov,
self.max_iter, self.tol, self.random_state)
self.max_iter, self.tol, self.random_state, self.alphas_cov)
for i in range(n_features))
coefs, tausq = zip(*results)
coefs = np.array(coefs)
Expand Down Expand Up @@ -1072,8 +1083,9 @@ def __init__(self, alpha='auto', n_alphas=100, alpha_cov='auto', n_alphas_cov=10
fit_intercept=True,
precompute=False, copy_X=True, max_iter=1000,
tol=1e-4, warm_start=False,
random_state=None, selection='cyclic', n_jobs=None):
self.estimator = DebiasedLasso(alpha=alpha, n_alphas=n_alphas, alpha_cov=alpha_cov, n_alphas_cov=n_alphas_cov,
random_state=None, selection='cyclic', n_jobs=None, *, alphas=None, alphas_cov=None):
self.estimator = DebiasedLasso(alpha=alpha, n_alphas=n_alphas, alphas=alphas, alpha_cov=alpha_cov,
n_alphas_cov=n_alphas_cov, alphas_cov=alphas_cov,
fit_intercept=fit_intercept,
precompute=precompute, copy_X=copy_X, max_iter=max_iter,
tol=tol, warm_start=warm_start,
Expand Down