Update dependencies

README.md

@@ -5,7 +5,7 @@
 [![Conda Version](https://img.shields.io/conda/vn/conda-forge/doubleml.svg)](https://anaconda.org/conda-forge/doubleml)
 [![codecov](https://codecov.io/gh/DoubleML/doubleml-for-py/branch/main/graph/badge.svg?token=0BjlFPgdGk)](https://codecov.io/gh/DoubleML/doubleml-for-py)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/1c08ec7d782c451784293c996537de14)](https://www.codacy.com/gh/DoubleML/doubleml-for-py/dashboard?utm_source=github.com&utm_medium=referral&utm_content=DoubleML/doubleml-for-py&utm_campaign=Badge_Grade)
-[![Python version](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)](https://www.python.org/)
+[![Python version](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)](https://www.python.org/)
 
 The Python package **DoubleML** provides an implementation of the double / debiased machine learning framework of
 [Chernozhukov et al. (2018)](https://doi.org/10.1111/ectj.12097).

doubleml/data/base_data.py

@@ -286,8 +286,8 @@ def from_arrays(
         elif not isinstance(force_all_d_finite, bool):
             raise TypeError("Invalid force_all_d_finite. " + "force_all_d_finite must be True, False or 'allow-nan'.")
 
-        x = check_array(x, ensure_2d=False, allow_nd=False, force_all_finite=force_all_x_finite)
-        d = check_array(d, ensure_2d=False, allow_nd=False, force_all_finite=force_all_x_finite)
+        x = check_array(x, ensure_2d=False, allow_nd=False, ensure_all_finite=force_all_x_finite)
+        d = check_array(d, ensure_2d=False, allow_nd=False, ensure_all_finite=force_all_x_finite)
         y = column_or_1d(y, warn=True)
 
         x = _assure_2d_array(x)

doubleml/did/did.py

@@ -201,8 +201,8 @@ def _check_data(self, obj_dml_data):
         return
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
 
         # nuisance g
         # get train indices for d == 0
@@ -372,8 +372,8 @@ def _sensitivity_element_est(self, preds):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
         # get train indices for d == 0 and d == 1
         smpls_d0, smpls_d1 = _get_cond_smpls(smpls, d)
 

doubleml/did/did_binary.py

@@ -429,8 +429,8 @@ def _preprocess_data(self, g_value, pre_t, eval_t):
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
 
         # Here: d is a binary treatment indicator
-        x, y = check_X_y(self._x_data_subset, self._y_data_subset, force_all_finite=False)
-        x, d = check_X_y(x, self._g_data_subset, force_all_finite=False)
+        x, y = check_X_y(self._x_data_subset, self._y_data_subset, ensure_all_finite=False)
+        x, d = check_X_y(x, self._g_data_subset, ensure_all_finite=False)
         # nuisance g
         # get train indices for d == 0
         smpls_d0, smpls_d1 = _get_cond_smpls(smpls, d)
@@ -570,8 +570,8 @@ def _score_elements(self, y, d, g_hat0, g_hat1, m_hat, p_hat):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._x_data_subset, self._y_data_subset, force_all_finite=False)
-        x, d = check_X_y(x, self._g_data_subset, force_all_finite=False)
+        x, y = check_X_y(self._x_data_subset, self._y_data_subset, ensure_all_finite=False)
+        x, d = check_X_y(x, self._g_data_subset, ensure_all_finite=False)
 
         # get train indices for d == 0 and d == 1
         smpls_d0, smpls_d1 = _get_cond_smpls(smpls, d)

doubleml/did/did_cs.py

@@ -212,9 +212,9 @@ def _check_data(self, obj_dml_data):
         return
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
-        x, t = check_X_y(x, self._dml_data.t, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
+        x, t = check_X_y(x, self._dml_data.t, ensure_all_finite=False)
 
         # THIS DIFFERS FROM THE PAPER due to stratified splitting this should be the same for each fold
         # nuisance estimates of the uncond. treatment prob.
@@ -547,9 +547,9 @@ def _sensitivity_element_est(self, preds):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
-        x, t = check_X_y(x, self._dml_data.t, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
+        x, t = check_X_y(x, self._dml_data.t, ensure_all_finite=False)
 
         if scoring_methods is None:
             scoring_methods = {"ml_g": None, "ml_m": None}

doubleml/did/did_cs_binary.py

@@ -435,9 +435,9 @@ def _estimate_conditional_g(
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
 
         # Here: d is a binary treatment indicator
-        x, y = check_X_y(X=self._x_data_subset, y=self._y_data_subset, force_all_finite=False)
-        _, d = check_X_y(x, self._g_data_subset, force_all_finite=False)  # (d is the G_indicator)
-        _, t = check_X_y(x, self._t_data_subset, force_all_finite=False)
+        x, y = check_X_y(X=self._x_data_subset, y=self._y_data_subset, ensure_all_finite=False)
+        _, d = check_X_y(x, self._g_data_subset, ensure_all_finite=False)  # (d is the G_indicator)
+        _, t = check_X_y(x, self._t_data_subset, ensure_all_finite=False)
 
         # THIS DIFFERS FROM THE PAPER due to stratified splitting this should be the same for each fold
         # nuisance estimates of the uncond. treatment prob.
@@ -621,9 +621,9 @@ def _score_elements(self, y, d, t, g_hat_d0_t0, g_hat_d0_t1, g_hat_d1_t0, g_hat_
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(X=self._x_data_subset, y=self._y_data_subset, force_all_finite=False)
-        _, d = check_X_y(x, self._g_data_subset, force_all_finite=False)  # (d is the G_indicator)
-        _, t = check_X_y(x, self._t_data_subset, force_all_finite=False)
+        x, y = check_X_y(X=self._x_data_subset, y=self._y_data_subset, ensure_all_finite=False)
+        _, d = check_X_y(x, self._g_data_subset, ensure_all_finite=False)  # (d is the G_indicator)
+        _, t = check_X_y(x, self._t_data_subset, ensure_all_finite=False)
 
         if scoring_methods is None:
             scoring_methods = {"ml_g": None, "ml_m": None}

doubleml/did/tests/test_did_aggregation_plot.py

@@ -1,5 +1,3 @@
-import warnings
-
 import matplotlib.pyplot as plt
 import numpy as np
 import pytest
@@ -180,13 +178,4 @@ def test_joint_ci_bootstrap_warning(mock_framework):
     with pytest.warns(UserWarning, match="Joint confidence intervals require bootstrapping"):
         _ = aggregation.plot_effects(joint=True)
 
-    # Verify that bootstrap was performed
-    assert aggregation.aggregated_frameworks.boot_t_stat is not None
-
-    # No warning should be raised when plotting again
-    with warnings.catch_warnings(record=True) as recorded_warnings:
-        warnings.simplefilter("always")  # Ensure all warnings are recorded
-        _ = aggregation.plot_effects(joint=True)
-
-    assert len(recorded_warnings) == 0
     plt.close("all")

doubleml/irm/apo.py

@@ -208,8 +208,8 @@ def _get_weights(self):
         return weights, weights_bar
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
         dx = np.column_stack((d, x))
         # use the treated indicator to get the correct sample splits
         treated = self.treated
@@ -361,8 +361,8 @@ def _sensitivity_element_est(self, preds):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
         dx = np.column_stack((d, x))
         # use the treated indicator to get the correct sample splits
         treated = self.treated

doubleml/irm/cvar.py

@@ -204,8 +204,8 @@ def _initialize_ml_nuisance_params(self):
         self._params = {learner: {key: [None] * self.n_rep for key in self._dml_data.d_cols} for learner in ["ml_g", "ml_m"]}
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
 
         # initialize nuisance predictions, targets and models
         g_hat = {
@@ -330,8 +330,8 @@ def ipw_score(theta):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
 
         if scoring_methods is None:
             scoring_methods = {"ml_g": None, "ml_m": None}

doubleml/irm/iivm.py

@@ -263,9 +263,9 @@ def _check_data(self, obj_dml_data):
         return
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, z = check_X_y(x, np.ravel(self._dml_data.z), force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, z = check_X_y(x, np.ravel(self._dml_data.z), ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
 
         # get train indices for z == 0 and z == 1
         smpls_z0, smpls_z1 = _get_cond_smpls(smpls, z)
@@ -448,9 +448,9 @@ def _score_elements(self, y, z, d, g_hat0, g_hat1, m_hat, r_hat0, r_hat1, smpls)
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, z = check_X_y(x, np.ravel(self._dml_data.z), force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, z = check_X_y(x, np.ravel(self._dml_data.z), ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
 
         # get train indices for z == 0 and z == 1
         smpls_z0, smpls_z1 = _get_cond_smpls(smpls, z)

doubleml/irm/irm.py

@@ -261,8 +261,8 @@ def _check_data(self, obj_dml_data):
         return
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
         # get train indices for d == 0 and d == 1
         smpls_d0, smpls_d1 = _get_cond_smpls(smpls, d)
         g0_external = external_predictions["ml_g0"] is not None
@@ -401,8 +401,8 @@ def _sensitivity_element_est(self, preds):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
         # get train indices for d == 0 and d == 1
         smpls_d0, smpls_d1 = _get_cond_smpls(smpls, d)
 

doubleml/irm/lpq.py

@@ -277,9 +277,9 @@ def _initialize_ml_nuisance_params(self):
         }
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
-        x, z = check_X_y(x, np.ravel(self._dml_data.z), force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
+        x, z = check_X_y(x, np.ravel(self._dml_data.z), ensure_all_finite=False)
 
         m_z = external_predictions["ml_m_z"] is not None
         m_d_d0 = external_predictions["ml_m_d_z0"] is not None
@@ -557,9 +557,9 @@ def ipw_score(theta):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
-        x, z = check_X_y(x, np.ravel(self._dml_data.z), force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
+        x, z = check_X_y(x, np.ravel(self._dml_data.z), ensure_all_finite=False)
 
         if scoring_methods is None:
             scoring_methods = {"ml_m_z": None, "ml_m_d_z0": None, "ml_m_d_z1": None, "ml_g_du_z0": None, "ml_g_du_z1": None}

doubleml/irm/pq.py

@@ -253,8 +253,8 @@ def _initialize_ml_nuisance_params(self):
         self._params = {learner: {key: [None] * self.n_rep for key in self._dml_data.d_cols} for learner in ["ml_g", "ml_m"]}
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
 
         g_external = external_predictions["ml_g"] is not None
         m_external = external_predictions["ml_m"] is not None
@@ -398,8 +398,8 @@ def ipw_score(theta):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
 
         if scoring_methods is None:
             scoring_methods = {"ml_g": None, "ml_m": None}

doubleml/irm/ssm.py

@@ -203,12 +203,12 @@ def _check_data(self, obj_dml_data):
         return
 
     def _nuisance_est(self, smpls, n_jobs_cv, external_predictions, return_models=False):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
-        x, s = check_X_y(x, self._dml_data.s, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
+        x, s = check_X_y(x, self._dml_data.s, ensure_all_finite=False)
 
         if self._score == "nonignorable":
-            z, _ = check_X_y(self._dml_data.z, y, force_all_finite=False)
+            z, _ = check_X_y(self._dml_data.z, y, ensure_all_finite=False)
             dx = np.column_stack((x, d, z))
         else:
             dx = np.column_stack((x, d))
@@ -427,12 +427,12 @@ def _score_elements(self, dtreat, dcontrol, g_d1, g_d0, pi, m, s, y):
     def _nuisance_tuning(
         self, smpls, param_grids, scoring_methods, n_folds_tune, n_jobs_cv, search_mode, n_iter_randomized_search
     ):
-        x, y = check_X_y(self._dml_data.x, self._dml_data.y, force_all_finite=False)
-        x, d = check_X_y(x, self._dml_data.d, force_all_finite=False)
-        x, s = check_X_y(x, self._dml_data.s, force_all_finite=False)
+        x, y = check_X_y(self._dml_data.x, self._dml_data.y, ensure_all_finite=False)
+        x, d = check_X_y(x, self._dml_data.d, ensure_all_finite=False)
+        x, s = check_X_y(x, self._dml_data.s, ensure_all_finite=False)
 
         if self._score == "nonignorable":
-            z, _ = check_X_y(self._dml_data.z, y, force_all_finite=False)
+            z, _ = check_X_y(self._dml_data.z, y, ensure_all_finite=False)
 
         if scoring_methods is None:
             scoring_methods = {"ml_g": None, "ml_pi": None, "ml_m": None}