DOC: Fix many aliases

doc/source/conf.py

@@ -23,6 +23,15 @@
 
 # More warnings
 nitpicky = True
+nitpick_ignore = []
+
+for line in open('nitpick-exceptions'):
+    if line.strip() == "" or line.startswith("#"):
+        continue
+    dtype, target = line.split(None, 1)
+    target = target.strip()
+    nitpick_ignore.append((dtype, target))
+
 
 # The short X.Y version
 full_version = parse(linearmodels.__version__)

doc/source/nitpick-exceptions

@@ -0,0 +1,5 @@
+py:class ArrayLike
+py:class IVDataLike
+py:class Float64Array
+py:class IntArray
+

linearmodels/asset_pricing/covariance.py

@@ -4,7 +4,8 @@
 
 from __future__ import annotations
 
-from numpy import empty, ndarray
+import numpy
+from numpy import empty
 from numpy.linalg import inv
 
 from linearmodels.iv.covariance import (
@@ -19,7 +20,7 @@ class _HACMixin:
     def __init__(self, kernel: str, bandwidth: float | None) -> None:
         self._kernel: str | None = None
         self._bandwidth: float | None = None  # pragma: no cover
-        self._moments: ndarray = empty((0,))  # pragma: no cover
+        self._moments: numpy.ndarray = empty((0,))  # pragma: no cover
         self._check_kernel(kernel)
         self._check_bandwidth(bandwidth)
 
@@ -98,8 +99,8 @@ def __init__(
         self,
         xe: Float64Array,
         *,
-        jacobian: ndarray | None = None,
-        inv_jacobian: ndarray | None = None,
+        jacobian: numpy.ndarray | None = None,
+        inv_jacobian: numpy.ndarray | None = None,
         center: bool = True,
         debiased: bool = False,
         df: int = 0,
@@ -231,8 +232,8 @@ def __init__(
         self,
         xe: Float64Array,
         *,
-        jacobian: ndarray | None = None,
-        inv_jacobian: ndarray | None = None,
+        jacobian: numpy.ndarray | None = None,
+        inv_jacobian: numpy.ndarray | None = None,
         kernel: str | None = None,
         bandwidth: float | None = None,
         center: bool = True,

linearmodels/asset_pricing/model.py

@@ -10,6 +10,7 @@
 from formulaic.materializers.types import NAAction
 import numpy as np
 from numpy.linalg import lstsq
+import pandas
 from pandas import DataFrame
 from scipy.optimize import minimize
 
@@ -134,7 +135,7 @@ def formula(self, value: str | None) -> None:
 
     @staticmethod
     def _prepare_data_from_formula(
-        formula: str, data: DataFrame, portfolios: DataFrame | None
+        formula: str, data: pandas.DataFrame, portfolios: pandas.DataFrame | None
     ) -> tuple[DataFrame, DataFrame, str]:
         orig_formula = formula
         na_action = NAAction("raise")
@@ -203,7 +204,11 @@ def __init__(self, portfolios: IVDataLike, factors: IVDataLike):
 
     @classmethod
     def from_formula(
-        cls, formula: str, data: DataFrame, *, portfolios: DataFrame | None = None
+        cls,
+        formula: str,
+        data: pandas.DataFrame,
+        *,
+        portfolios: pandas.DataFrame | None = None,
     ) -> TradedFactorModel:
         """
         Parameters
@@ -517,9 +522,9 @@ def __init__(
     def from_formula(
         cls,
         formula: str,
-        data: DataFrame,
+        data: pandas.DataFrame,
         *,
-        portfolios: DataFrame | None = None,
+        portfolios: pandas.DataFrame | None = None,
         risk_free: bool = False,
         sigma: ArrayLike | None = None,
     ) -> LinearFactorModel:
@@ -818,9 +823,9 @@ def __init__(
     def from_formula(
         cls,
         formula: str,
-        data: DataFrame,
+        data: pandas.DataFrame,
         *,
-        portfolios: DataFrame | None = None,
+        portfolios: pandas.DataFrame | None = None,
         risk_free: bool = False,
     ) -> LinearFactorModelGMM:
         """

linearmodels/iv/_utility.py

@@ -7,7 +7,9 @@
 from formulaic.formula import Formula
 from formulaic.materializers.types import NAAction as fNAAction
 from formulaic.utils.context import capture_context
+import numpy
 import numpy as np
+import pandas
 from pandas import DataFrame
 
 from linearmodels.typing import Float64Array
@@ -91,7 +93,7 @@ class IVFormulaParser:
     def __init__(
         self,
         formula: str,
-        data: DataFrame,
+        data: pandas.DataFrame,
         eval_env: int = 2,
         context: Mapping[str, Any] | None = None,
     ):
@@ -222,5 +224,5 @@ def components(self) -> dict[str, str]:
         return self._components
 
     @staticmethod
-    def _empty_check(arr: DataFrame) -> DataFrame | None:
+    def _empty_check(arr: pandas.DataFrame) -> DataFrame | None:
         return None if arr.shape[1] == 0 else arr

linearmodels/iv/absorbing.py

@@ -8,7 +8,6 @@
 from numpy import (
     any as npany,
     arange,
-    array,
     asarray,
     ascontiguousarray,
     average,
@@ -31,6 +30,7 @@
     zeros,
 )
 from numpy.linalg import lstsq
+import pandas
 from pandas import Categorical, CategoricalDtype, DataFrame, Series
 import scipy.sparse as sp
 from scipy.sparse.linalg import lsmr
@@ -211,7 +211,9 @@ def category_product(cats: AnyPandas) -> Series:
     return Series(Categorical(codes), index=cats.index)
 
 
-def category_interaction(cat: Series, precondition: bool = True) -> sp.csc_matrix:
+def category_interaction(
+    cat: pandas.Series, precondition: bool = True
+) -> sp.csc_matrix:
     """
     Parameters
     ----------

linearmodels/iv/data.py

@@ -8,6 +8,7 @@
 from typing import Any, Union
 
 import numpy as np
+import pandas
 import pandas as pd
 from pandas.api.types import is_numeric_dtype, is_string_dtype
 
@@ -26,7 +27,7 @@ def convert_columns(s: pd.Series, drop_first: bool) -> AnyPandas:
     return s
 
 
-def expand_categoricals(x: pd.DataFrame, drop_first: bool) -> pd.DataFrame:
+def expand_categoricals(x: pandas.DataFrame, drop_first: bool) -> pd.DataFrame:
     if x.shape[1] == 0:
         return x
     return pd.concat(

linearmodels/iv/gmm.py

@@ -6,7 +6,8 @@
 
 from typing import Any
 
-from numpy import asarray, ndarray, unique
+import numpy
+from numpy import asarray, unique
 from numpy.linalg import inv
 
 from linearmodels.iv.covariance import (
@@ -75,7 +76,7 @@ def weight_matrix(
         return w
 
     @property
-    def config(self) -> dict[str, str | bool | ndarray | int | None]:
+    def config(self) -> dict[str, str | bool | numpy.ndarray | int | None]:
         """
         Weight estimator configuration
 
@@ -239,7 +240,7 @@ def weight_matrix(
         return s
 
     @property
-    def config(self) -> dict[str, str | bool | ndarray | int | None]:
+    def config(self) -> dict[str, str | bool | numpy.ndarray | int | None]:
         """
         Weight estimator configuration
 
@@ -324,7 +325,7 @@ def weight_matrix(
         return s
 
     @property
-    def config(self) -> dict[str, str | bool | ndarray | int | None]:
+    def config(self) -> dict[str, str | bool | numpy.ndarray | int | None]:
         """
         Weight estimator configuration
 
@@ -466,7 +467,9 @@ def cov(self) -> Float64Array:
         return (c + c.T) / 2
 
     @property
-    def config(self) -> dict[str, str | bool | ndarray | int | None]:
-        conf: dict[str, str | bool | ndarray | int | None] = {"debiased": self.debiased}
+    def config(self) -> dict[str, str | bool | numpy.ndarray | int | None]:
+        conf: dict[str, str | bool | numpy.ndarray | int | None] = {
+            "debiased": self.debiased
+        }
         conf.update(self._cov_config)
         return conf

linearmodels/iv/model.py

@@ -26,6 +26,7 @@
     squeeze,
 )
 from numpy.linalg import eigvalsh, inv, matrix_rank, pinv
+import pandas
 from pandas import DataFrame, Series, concat
 from scipy.optimize import minimize
 
@@ -243,7 +244,7 @@ def predict(
         *,
         exog: IVDataLike | None = None,
         endog: IVDataLike | None = None,
-        data: DataFrame | None = None,
+        data: pandas.DataFrame | None = None,
         eval_env: int = 4,
     ) -> DataFrame:
         """
@@ -776,7 +777,7 @@ def __init__(
     @staticmethod
     def from_formula(
         formula: str,
-        data: DataFrame,
+        data: pandas.DataFrame,
         *,
         weights: IVDataLike | None = None,
         fuller: float = 0,
@@ -889,7 +890,7 @@ def __init__(
 
     @staticmethod
     def from_formula(
-        formula: str, data: DataFrame, *, weights: IVDataLike | None = None
+        formula: str, data: pandas.DataFrame, *, weights: IVDataLike | None = None
     ) -> IV2SLS:
         """
         Parameters
@@ -1106,7 +1107,7 @@ def __init__(
     @staticmethod
     def from_formula(
         formula: str,
-        data: DataFrame,
+        data: pandas.DataFrame,
         *,
         weights: IVDataLike | None = None,
         weight_type: str = "robust",
@@ -1214,7 +1215,7 @@ def fit(
             Convergence criteria.  Measured as covariance normalized change in
             parameters across iterations where the covariance estimator is
             based on the first step parameter estimates.
-        initial_weight : ndarray
+        initial_weight : numpy.ndarray
             Initial weighting matrix to use in the first step.  If not
             specified, uses the average outer-product of the set containing
             the exogenous variables and instruments.
@@ -1379,7 +1380,7 @@ def __init__(
     @staticmethod
     def from_formula(
         formula: str,
-        data: DataFrame,
+        data: pandas.DataFrame,
         *,
         weights: IVDataLike | None = None,
         weight_type: str = "robust",
@@ -1536,7 +1537,7 @@ def estimate_parameters(
     def fit(
         self,
         *,
-        starting: Float64Array | Series | None = None,
+        starting: Float64Array | pandas.Series | None = None,
         display: bool = False,
         cov_type: str = "robust",
         debiased: bool = False,
@@ -1664,7 +1665,7 @@ def __init__(
 def _gmm_model_from_formula(
     cls: type[IVGMM] | type[IVGMMCUE],
     formula: str,
-    data: DataFrame,
+    data: pandas.DataFrame,
     weights: IVDataLike | None,
     weight_type: str,
     **weight_config: Any,

linearmodels/iv/results.py

@@ -11,6 +11,7 @@
 from functools import cached_property
 from typing import Any, Union
 
+import numpy
 from numpy import (
     array,
     asarray,
@@ -25,6 +26,7 @@
     squeeze,
 )
 from numpy.linalg import inv
+import pandas
 from pandas import DataFrame, Series, concat, to_numeric
 import scipy.stats as stats
 from statsmodels.iolib.summary import SimpleTable, fmt_2cols, fmt_params
@@ -407,8 +409,8 @@ def _update_extra_text(self, extra_text: list[str]) -> list[str]:
 
     def wald_test(
         self,
-        restriction: DataFrame | ndarray | None = None,
-        value: Series | ndarray | None = None,
+        restriction: pandas.DataFrame | numpy.ndarray | None = None,
+        value: pandas.Series | numpy.ndarray | None = None,
         *,
         formula: str | list[str] | dict[str, float] | None = None,
     ) -> WaldTestStatistic:
@@ -524,7 +526,7 @@ def predict(
         exog: ArrayLike | None = None,
         endog: ArrayLike | None = None,
         *,
-        data: DataFrame | None = None,
+        data: pandas.DataFrame | None = None,
         fitted: bool = True,
         idiosyncratic: bool = False,
         missing: bool = False,

linearmodels/panel/covariance.py

@@ -5,6 +5,7 @@
 
 import numpy as np
 from numpy.linalg import inv
+import pandas
 from pandas import DataFrame, MultiIndex
 
 from linearmodels.iv.covariance import (
@@ -669,7 +670,7 @@ def __init__(
         y: Float64Array,
         x: Float64Array,
         params: Float64Array,
-        all_params: DataFrame,
+        all_params: pandas.DataFrame,
         *,
         debiased: bool = False,
         bandwidth: float | None = None,