Merge pull request #346 from bashtage/additional-typing-clean

CLN: Improve typing

arch/bootstrap/base.py

@@ -71,7 +71,7 @@ def _loo_jackknife(
     func: Callable[..., NDArray],
     nobs: int,
     args: Sequence[ArrayLike],
-    kwargs: ArrayLike,
+    kwargs: Dict[str, ArrayLike],
 ) -> NDArray:
     """
     Leave one out jackknife estimation
@@ -137,7 +137,9 @@ def _add_extra_kwargs(
     if extra_kwargs is None:
         return kwargs
     else:
-        return dict(list(kwargs.items()) + list(extra_kwargs.items()))
+        kwargs_copy = kwargs.copy()
+        kwargs_copy.update(extra_kwargs)
+        return kwargs_copy
 
 
 class IIDBootstrap(object, metaclass=DocStringInheritor):
@@ -666,7 +668,7 @@ def clone(self, *args: ArrayLike, **kwargs: ArrayLike) -> "IIDBootstrap":
         bs
             Bootstrap instance
         """
-        pos_arguments = copy.deepcopy(self._parameters)
+        pos_arguments: List[Union[int, ArrayLike]] = copy.deepcopy(self._parameters)
         pos_arguments.extend(args)
         bs = self.__class__(*pos_arguments, **kwargs)
         if self._seed is not None:
@@ -1088,7 +1090,7 @@ def update_indices(self) -> Tuple[List[NDArray], Dict[str, NDArray]]:
         return pos_indices, kw_indices
 
     @property
-    def index(self) -> NDArray:
+    def index(self) -> Tuple[List[NDArray], Dict[str, NDArray]]:
         """
         Returns the current index of the bootstrap
 

arch/data/binary/__init__.py

@@ -5,7 +5,7 @@
 
 def load() -> DataFrame:
     """
-    Load the graduate school admissions dataused in the examples
+    Load the graduate school admissions data used in the examples
 
     Returns
     -------

arch/tests/bootstrap/test_bootstrap.py

@@ -606,7 +606,7 @@ def test_apply(bs_setup):
     bs.seed(23456)
 
     results = bs.apply(bs_setup.func, 1000)
-    bs.reset(23456)
+    bs.reset(True)
     direct_results = []
     for pos, _ in bs.bootstrap(1000):
         direct_results.append(bs_setup.func(*pos))
@@ -619,7 +619,7 @@ def test_apply_series(bs_setup):
     bs.seed(23456)
 
     results = bs.apply(bs_setup.func, 1000)
-    bs.reset(23456)
+    bs.reset(True)
     direct_results = []
     for pos, _ in bs.bootstrap(1000):
         direct_results.append(bs_setup.func(*pos))

arch/tests/univariate/test_mean.py

@@ -123,7 +123,7 @@ def test_constant_mean(self):
         direct = pd.DataFrame(
             index=np.arange(self.y.shape[0]),
             columns=["h.{0:>02d}".format(i + 1) for i in range(20)],
-            dtype=np.float64,
+            dtype="double",
         )
         direct.iloc[20:, :] = res.params.iloc[0]
         # TODO
@@ -156,7 +156,7 @@ def test_zero_mean(self):
         direct = pd.DataFrame(
             index=np.arange(self.y.shape[0]),
             columns=["h.{0:>02d}".format(i + 1) for i in range(99)],
-            dtype=np.float64,
+            dtype="double",
         )
         direct.iloc[:, :] = 0.0
         assert isinstance(forecasts, ARCHModelForecast)
@@ -271,7 +271,7 @@ def test_har(self):
         direct = pd.DataFrame(
             index=np.arange(t),
             columns=["h." + str(i + 1) for i in range(6)],
-            dtype=np.float64,
+            dtype="float64",
         )
 
         params = np.asarray(res.params)
@@ -303,7 +303,7 @@ def test_har(self):
         direct = pd.DataFrame(
             index=self.y_series.index,
             columns=["h." + str(i + 1) for i in range(6)],
-            dtype=np.float64,
+            dtype="float64",
         )
         forecasts = res.forecast(horizon=6)
         params = np.asarray(res.params)
@@ -393,7 +393,7 @@ def test_ar(self):
         direct = pd.DataFrame(
             index=np.arange(y.shape[0]),
             columns=["h." + str(i + 1) for i in range(5)],
-            dtype=np.float64,
+            dtype="float64",
         )
         params = res.params.iloc[:-1]
         for i in range(2, y.shape[0]):
@@ -458,10 +458,10 @@ def test_ar_plot(self):
 
         res.plot(scale=360)
         res.hedgehog_plot(start=500)
-        res.hedgehog_plot(start=500, type="mean")
-        res.hedgehog_plot(type="volatility")
+        res.hedgehog_plot(start=500, plot_type="mean")
+        res.hedgehog_plot(plot_type="volatility")
         res.hedgehog_plot(start=500, method="simulation", simulations=100)
-        res.hedgehog_plot(type="volatility", method="bootstrap")
+        res.hedgehog_plot(plot_type="volatility", method="bootstrap")
 
     def test_arch_arx(self):
         self.rng.seed(12345)

arch/tests/univariate/test_moment.py

@@ -1,4 +1,4 @@
-from numpy import exp, inf, log, nan, pi
+from numpy import exp, inf, log, nan, ones_like, pi
 from numpy.testing import assert_almost_equal, assert_equal
 import pytest
 from scipy.integrate import quad
@@ -43,7 +43,8 @@ def test_moment(dist, params):
 
     # verify moments that exist
     def f(x, n):
-        return (x ** n) * exp(dist.loglikelihood(params, x, 1, True))
+        sigma2 = ones_like(x)
+        return (x ** n) * exp(dist.loglikelihood(params, x, sigma2, True))
 
     for n in range(6):  # moments 0-5
 

arch/unitroot/critical_values/simulation/dfgls_simulation_process.py

@@ -120,7 +120,7 @@
     # Compute tau star
     err_large = res_large.resid
     # Missing 1 parameter here, replace with 0
-    params = np.append(res_small.params, 0.0)
+    params = np.append(np.asarray(res_small.params), np.zeros(1))
     err_small = lhs_large - rhs_large.dot(params)
     # Find the location that minimizes the total absolute error
     m = lhs_large.shape[0]

arch/unitroot/unitroot.py

@@ -1820,7 +1820,7 @@ def auto_bandwidth(
     for i in range(n + 1):
         a = list(y[i:])
         b = list(y[: len(y) - i])
-        sig[i] = sum([i * j for (i, j) in zip(a, b)])
+        sig[i] = int(sum([i * j for (i, j) in zip(a, b)]))
 
     sigma_m1 = sig[1 : len(sig)]  # sigma without the 1st element
     s0 = sig[0] + 2 * sum(sigma_m1)

arch/univariate/base.py

@@ -427,7 +427,7 @@ def _parse_parameters(self, x: ArrayLike) -> Tuple[NDArray, NDArray, NDArray]:
 
     def fix(
         self,
-        params: ArrayLike1D,
+        params: Union[Sequence[float], ArrayLike1D],
         first_obs: Union[int, DateLike] = None,
         last_obs: Union[int, DateLike] = None,
     ) -> "ARCHModelFixedResult":
@@ -464,7 +464,7 @@ def fix(
 
         var_bounds = v.variance_bounds(resids)
 
-        params = np.asarray(params)
+        params = ensure1d(params, "params", False)
         loglikelihood = -1.0 * self._loglikelihood(params, sigma2, backcast, var_bounds)
 
         mp, vp, dp = self._parse_parameters(params)
@@ -1453,7 +1453,7 @@ def hedgehog_plot(
         >>> sim_data.index = pd.date_range('2000-01-01',periods=250)
         >>> am = arch_model(sim_data['data'],mean='HAR',lags=[1,5,22],  vol='Constant')
         >>> res = am.fit()
-        >>> fig = res.hedgehog_plot(type='mean')
+        >>> fig = res.hedgehog_plot(plot_type='mean')
         """
         import matplotlib.pyplot as plt
 
@@ -1888,7 +1888,7 @@ def _format_forecasts(
     horizon = values.shape[1]
     format_str = "{0:>0" + str(int(np.ceil(np.log10(horizon + 0.5)))) + "}"
     columns = ["h." + format_str.format(h + 1) for h in range(horizon)]
-    forecasts = DataFrame(values, index=index, columns=columns, dtype=np.float64)
+    forecasts = DataFrame(values, index=index, columns=columns, dtype="float")
     return forecasts
 
 
@@ -1917,30 +1917,30 @@ class ARCHModelForecastSimulation(object):
 
     def __init__(
         self,
-        values: DataFrame,
-        residuals: DataFrame,
-        variances: DataFrame,
-        residual_variances: DataFrame,
+        values: NDArray,
+        residuals: NDArray,
+        variances: NDArray,
+        residual_variances: NDArray,
     ) -> None:
         self._values = values
         self._residuals = residuals
         self._variances = variances
         self._residual_variances = residual_variances
 
     @property
-    def values(self) -> DataFrame:
+    def values(self) -> NDArray:
         return self._values
 
     @property
-    def residuals(self) -> DataFrame:
+    def residuals(self) -> NDArray:
         return self._residuals
 
     @property
-    def variances(self) -> DataFrame:
+    def variances(self) -> NDArray:
         return self._variances
 
     @property
-    def residual_variances(self) -> DataFrame:
+    def residual_variances(self) -> NDArray:
         return self._residual_variances