Move _feols_input_checks functions from feols.py to dev_utils.py

.gitignore

@@ -9,7 +9,7 @@ dist
 .mypy_cache
 .pytest_cache
 .vscode
-.coverage
+.coverage*
 *.pyc
 __pycache__/
 #docs/figures

pyfixest/estimation/feols_.py

@@ -2506,38 +2506,6 @@ def update(
         return beta_n_plus_1
 
 
-def _feols_input_checks(Y: np.ndarray, X: np.ndarray, weights: np.ndarray):
-    """
-    Perform basic checks on the input matrices Y and X for the FEOLS.
-
-    Parameters
-    ----------
-    Y : np.ndarray
-        FEOLS input matrix Y.
-    X : np.ndarray
-        FEOLS input matrix X.
-    weights : np.ndarray
-        FEOLS weights.
-
-    Returns
-    -------
-    None
-    """
-    if not isinstance(Y, (np.ndarray)):
-        raise TypeError("Y must be a numpy array.")
-    if not isinstance(X, (np.ndarray)):
-        raise TypeError("X must be a numpy array.")
-    if not isinstance(weights, (np.ndarray)):
-        raise TypeError("weights must be a numpy array.")
-
-    if Y.ndim != 2:
-        raise ValueError("Y must be a 2D array")
-    if X.ndim != 2:
-        raise ValueError("X must be a 2D array")
-    if weights.ndim != 2:
-        raise ValueError("weights must be a 2D array")
-
-
 def _get_vcov_type(vcov: str, fval: str):
     """
     Get variance-covariance matrix type.

pyfixest/estimation/fepois_.py

@@ -170,15 +170,14 @@ def prepare_model_matrix(self):
             self._fe.drop(na_separation, axis=0, inplace=True)
             self._data.drop(na_separation, axis=0, inplace=True)
             self._N = self._Y.shape[0]
-            self._N_rows = self._N # ok as weights not supported
+            self._N_rows = self._N  # ok as weights not supported
 
             self.na_index = np.concatenate([self.na_index, np.array(na_separation)])
             self.n_separation_na = len(na_separation)
             # possible to have dropped fixed effects level due to separation
             self._k_fe = self._fe.nunique(axis=0) if self._has_fixef else None
             self._n_fe = np.sum(self._k_fe > 1) if self._has_fixef else 0
 
-
     def to_array(self):
         "Turn estimation DataFrames to np arrays."
         self._Y, self._X, self._Z = (

pyfixest/estimation/vcov_utils.py

@@ -60,6 +60,7 @@ def _count_G_for_ssc_correction(
 
     return G
 
+
 def _get_vcov_type(
     vcov: Union[str, dict[str, str], None], fval: str
 ) -> Union[str, dict[str, str]]:
@@ -96,7 +97,6 @@ def _get_vcov_type(
     return vcov_type  # type: ignore
 
 
-
 def _prepare_twoway_clustering(clustervar: list, cluster_df: pd.DataFrame):
     cluster_one = clustervar[0]
     cluster_two = clustervar[1]

pyfixest/utils/dev_utils.py

@@ -191,3 +191,35 @@
     level = match.group(2)
 
     return f"C({variable})", level
+
+
+def _feols_input_checks(Y: np.ndarray, X: np.ndarray, weights: np.ndarray):
+    """
+    Perform basic checks on the input matrices Y and X for the FEOLS.
+
+    Parameters
+    ----------
+    Y : np.ndarray
+        FEOLS input matrix Y.
+    X : np.ndarray
+        FEOLS input matrix X.
+    weights : np.ndarray
+        FEOLS weights.
+
+    Returns
+    -------
+    None
+    """
+    if not isinstance(Y, (np.ndarray)):
+        raise TypeError("Y must be a numpy array.")
+    if not isinstance(X, (np.ndarray)):
+        raise TypeError("X must be a numpy array.")
+    if not isinstance(weights, (np.ndarray)):
+        raise TypeError("weights must be a numpy array.")
+
+    if Y.ndim != 2:
+        raise ValueError("Y must be a 2D array")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D array")
+    if weights.ndim != 2:
+        raise ValueError("weights must be a 2D array")