formatting

Author: SvenKlaassen

doubleml/did/did_cs.py

@@ -59,7 +59,8 @@ class DoubleMLDIDCS(LinearScoreMixin, DoubleML):
         Default is ``True``.
 
     Examples
-    --------    >>> import numpy as np
+    --------
+    >>> import numpy as np
     >>> import doubleml as dml
     >>> from doubleml.did.datasets import make_did_SZ2020
     >>> from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

doubleml/plm/datasets/dgp_lplr_LZZ2020.py

@@ -10,8 +10,7 @@
 _dml_data_alias = _get_dml_data_alias()
 
 
-def make_lplr_LZZ2020(
-    n_obs=500, dim_x=20, alpha=0.5, return_type="DoubleMLData", balanced_r0=True, treatment="continuous"):
+def make_lplr_LZZ2020(n_obs=500, dim_x=20, alpha=0.5, return_type="DoubleMLData", balanced_r0=True, treatment="continuous"):
     r"""
     Generates synthetic data for a logistic partially linear regression model, as in Liu et al. (2021),
     designed for use in double/debiased machine learning applications.
@@ -48,6 +47,7 @@ def make_lplr_LZZ2020(
 
     return_type : str, default="DoubleMLData"
         Determines the return format. One of:
+
         - 'DoubleMLData' or DoubleMLData: returns a ``DoubleMLData`` object.
         - 'DataFrame', 'pd.DataFrame' or pd.DataFrame: returns a ``pandas.DataFrame``.
         - 'array', 'np.ndarray', 'np.array' or np.ndarray: returns tuple of numpy arrays (x, y, d, p).
@@ -60,6 +60,7 @@ def make_lplr_LZZ2020(
     treatment : str, default="continuous"
         Type of treatment variable. One of "continuous", "binary", or "binary_unbalanced".
         Determines how the treatment d is generated from a_0(x):
+
         - "continuous": d = a_0(x) (continuous treatment).
         - "binary":    d ~ Bernoulli( sigmoid(a_0(x) - mean(a_0(x))) ) .
         - "binary_unbalanced": d ~ Bernoulli( sigmoid(a_0(x)) ).

doubleml/plm/lplr.py

@@ -28,12 +28,12 @@ class DoubleMLLPLR(NonLinearScoreMixin, DoubleML):
 
     ml_M : estimator implementing ``fit()`` and ``predict_proba()``
         A machine learner implementing ``fit()`` and ``predict_proba()`` methods (e.g.
-        :py:class:`sklearn.ensemble.RandomForestClassifier`) for the nuisance function 
+        :py:class:`sklearn.ensemble.RandomForestClassifier`) for the nuisance function
         :math:`M_0(D, X) = P[Y = 1 | D, X]`.
 
     ml_t : estimator implementing ``fit()`` and ``predict()``
         A machine learner implementing ``fit()`` and ``predict()`` methods (e.g.
-        :py:class:`sklearn.ensemble.RandomForestRegressor`) for the auxiliary regression 
+        :py:class:`sklearn.ensemble.RandomForestRegressor`) for the auxiliary regression
         used to predict log-odds :math:`t_0(X) = E[W | X]` where :math:`W = \\text{logit}(M_0(D, X))`.
 
     ml_m : estimator implementing ``fit()`` and ``predict()`` or ``predict_proba()``