Unsafe fixes

adaptive/learner/average_learner.py

@@ -1,22 +1,24 @@
 from __future__ import annotations
 
 from math import sqrt
-from typing import Callable
+from typing import TYPE_CHECKING, Callable
 
 import cloudpickle
 import numpy as np
 
 from adaptive.learner.base_learner import BaseLearner
 from adaptive.notebook_integration import ensure_holoviews
-from adaptive.types import Float, Int, Real
 from adaptive.utils import (
     assign_defaults,
     cache_latest,
     partial_function_from_dataframe,
 )
 
+if TYPE_CHECKING:
+    from adaptive.types import Float, Int, Real
+
 try:
-    import pandas
+    import pandas as pd
 
     with_pandas = True
 
@@ -47,6 +49,7 @@ class AverageLearner(BaseLearner):
         Points that still have to be evaluated.
     npoints : int
         Number of evaluated points.
+
     """
 
     def __init__(
@@ -57,7 +60,8 @@ def __init__(
         min_npoints: int = 2,
     ) -> None:
         if atol is None and rtol is None:
-            raise Exception("At least one of `atol` and `rtol` should be set.")
+            msg = "At least one of `atol` and `rtol` should be set."
+            raise Exception(msg)
         if atol is None:
             atol = np.inf
         if rtol is None:
@@ -92,7 +96,7 @@ def to_dataframe(  # type: ignore[override]
         function_prefix: str = "function.",
         seed_name: str = "seed",
         y_name: str = "y",
-    ) -> pandas.DataFrame:
+    ) -> pd.DataFrame:
         """Return the data as a `pandas.DataFrame`.
 
         Parameters
@@ -116,10 +120,12 @@ def to_dataframe(  # type: ignore[override]
         ------
         ImportError
             If `pandas` is not installed.
+
         """
         if not with_pandas:
-            raise ImportError("pandas is not installed.")
-        df = pandas.DataFrame(sorted(self.data.items()), columns=[seed_name, y_name])
+            msg = "pandas is not installed."
+            raise ImportError(msg)
+        df = pd.DataFrame(sorted(self.data.items()), columns=[seed_name, y_name])
         df.attrs["inputs"] = [seed_name]
         df.attrs["output"] = y_name
         if with_default_function_args:
@@ -128,12 +134,12 @@ def to_dataframe(  # type: ignore[override]
 
     def load_dataframe(  # type: ignore[override]
         self,
-        df: pandas.DataFrame,
+        df: pd.DataFrame,
         with_default_function_args: bool = True,
         function_prefix: str = "function.",
         seed_name: str = "seed",
         y_name: str = "y",
-    ):
+    ) -> None:
         """Load data from a `pandas.DataFrame`.
 
         If ``with_default_function_args`` is True, then ``learner.function``'s
@@ -153,11 +159,14 @@ def load_dataframe(  # type: ignore[override]
             The ``seed_name`` used in ``to_dataframe``, by default "seed"
         y_name : str, optional
             The ``y_name`` used in ``to_dataframe``, by default "y"
+
         """
         self.tell_many(df[seed_name].values, df[y_name].values)
         if with_default_function_args:
             self.function = partial_function_from_dataframe(
-                self.function, df, function_prefix
+                self.function,
+                df,
+                function_prefix,
             )
 
     def ask(self, n: int, tell_pending: bool = True) -> tuple[list[int], list[Float]]:
@@ -168,7 +177,7 @@ def ask(self, n: int, tell_pending: bool = True) -> tuple[list[int], list[Float]
             points = list(
                 set(range(self.n_requested + n))
                 - set(self.data)
-                - set(self.pending_points)
+                - set(self.pending_points),
             )[:n]
 
         loss_improvements = [self._loss_improvement(n) / n] * n
@@ -199,7 +208,8 @@ def mean(self) -> Float:
     @property
     def std(self) -> Float:
         """The corrected sample standard deviation of the values
-        in `data`."""
+        in `data`.
+        """
         n = self.npoints
         if n < self.min_npoints:
             return np.inf
@@ -211,10 +221,7 @@ def std(self) -> Float:
 
     @cache_latest
     def loss(self, real: bool = True, *, n=None) -> Float:
-        if n is None:
-            n = self.npoints if real else self.n_requested
-        else:
-            n = n
+        n = (self.npoints if real else self.n_requested) if n is None else n
         if n < self.min_npoints:
             return np.inf
         standard_error = self.std / sqrt(n)
@@ -232,7 +239,7 @@ def _loss_improvement(self, n: int) -> Float:
         else:
             return np.inf
 
-    def remove_unfinished(self):
+    def remove_unfinished(self) -> None:
         """Remove uncomputed data from the learner."""
         self.pending_points = set()
 
@@ -242,7 +249,9 @@ def plot(self):
         Returns
         -------
         holoviews.element.Histogram
-            A histogram of the evaluated data."""
+        A histogram of the evaluated data.
+
+        """
         hv = ensure_holoviews()
         vals = [v for v in self.data.values() if v is not None]
         if not vals: