improve input processing for StatsModelsLinearRegression

econml/sklearn_extensions/linear_model.py

@@ -19,7 +19,7 @@
 from collections.abc import Iterable
 from scipy.stats import norm
 from econml.sklearn_extensions.model_selection import WeightedKFold, WeightedStratifiedKFold
-from econml.utilities import ndim, shape, reshape, _safe_norm_ppf
+from econml.utilities import ndim, shape, reshape, _safe_norm_ppf, check_input_arrays
 from sklearn import clone
 from sklearn.linear_model import LinearRegression, LassoCV, MultiTaskLassoCV, Lasso, MultiTaskLasso
 from sklearn.metrics import r2_score
@@ -1683,6 +1683,8 @@ def __init__(self, fit_intercept=True, cov_type="HC0"):
     def _check_input(self, X, y, sample_weight, freq_weight, sample_var):
         """Check dimensions and other assertions."""
 
+        X, y, sample_weight, freq_weight, sample_var = check_input_arrays(
+            X, y, sample_weight, freq_weight, sample_var, dtype='numeric')
         if X is None:
             X = np.empty((y.shape[0], 0))
         if self.fit_intercept:

econml/tests/test_statsmodels.py

@@ -327,6 +327,25 @@ def true_effect(x):
             assert np.all(np.abs(est.intercept_ - lr.intercept_) <
                           1e-12), "{}, {}".format(est.intercept_, lr.intercept_)
 
+    def test_o_dtype(self):
+        """ Testing that the models still work when the np arrays are of O dtype """
+        np.random.seed(123)
+        n = 1000
+        d = 3
+
+        X = np.random.normal(size=(n, d)).astype('O')
+        y = np.random.normal(size=n).astype('O')
+
+        est = OLS().fit(X, y)
+        lr = LinearRegression().fit(X, y)
+        assert np.all(np.abs(est.coef_ - lr.coef_) < 1e-12), "{}, {}".format(est.coef_, lr.coef_)
+        assert np.all(np.abs(est.intercept_ - lr.intercept_) < 1e-12), "{}, {}".format(est.coef_, lr.intercept_)
+
+        est = OLS(fit_intercept=False).fit(X, y)
+        lr = LinearRegression(fit_intercept=False).fit(X, y)
+        assert np.all(np.abs(est.coef_ - lr.coef_) < 1e-12), "{}, {}".format(est.coef_, lr.coef_)
+        assert np.all(np.abs(est.intercept_ - lr.intercept_) < 1e-12), "{}, {}".format(est.coef_, lr.intercept_)
+
     def test_inference(self):
         """ Testing that we recover the expected standard errors and confidence intervals in a known example """
 

econml/utilities.py

@@ -514,7 +514,7 @@ def check_inputs(Y, T, X, W=None, multi_output_T=True, multi_output_Y=True):
     return Y, T, X, W
 
 
-def check_input_arrays(*args, validate_len=True, force_all_finite=True):
+def check_input_arrays(*args, validate_len=True, force_all_finite=True, dtype=None):
     """Cast input sequences into numpy arrays.
 
     Only inputs that are sequence-like will be converted, all other inputs will be left as is.
@@ -531,6 +531,13 @@ def check_input_arrays(*args, validate_len=True, force_all_finite=True):
     force_all_finite : bool (default=True)
         Whether to allow inf and nan in input arrays.
 
+    dtype : 'numeric', type, list of type or None (default=None)
+        Argument passed to sklearn.utils.check_array.
+        Specifies data type of result. If None, the dtype of the input is preserved.
+        If "numeric", dtype is preserved unless array.dtype is object.
+        If dtype is a list of types, conversion on the first type is only
+        performed if the dtype of the input is not in the list.
+
     Returns
     -------
     args: array-like
@@ -541,7 +548,7 @@ def check_input_arrays(*args, validate_len=True, force_all_finite=True):
     args = list(args)
     for i, arg in enumerate(args):
         if np.ndim(arg) > 0:
-            new_arg = check_array(arg, dtype=None, ensure_2d=False, accept_sparse=True,
+            new_arg = check_array(arg, dtype=dtype, ensure_2d=False, accept_sparse=True,
                                   force_all_finite=force_all_finite)
             if not force_all_finite:
                 # For when checking input values is disabled