Parameter Fitter: Type annotations

Author: VesnaT

Orange/base.py

@@ -1,10 +1,10 @@
 import inspect
 import itertools
-from collections import namedtuple
 from collections.abc import Iterable
 import re
 import warnings
-from typing import Callable, Dict, Optional, List
+from types import NoneType
+from typing import Callable, Optional, NamedTuple, Union, Type
 
 import numpy as np
 import scipy
@@ -88,9 +88,15 @@ class Learner(ReprableWithPreprocessors):
     #: A sequence of data preprocessors to apply on data prior to
     #: fitting the model
     preprocessors = ()
-    FittedParameter = namedtuple(
-        "FittedParameter",
-        ["parameter_name", "label", "tick_label", "type", "min", "max"]
+    FittedParameter = NamedTuple(
+        "FittedParameter", [
+            ("name", str),
+            ("label", str),
+            ("tick_label", str),
+            ("type", Type),
+            ("min", Union[int, NoneType]),
+            ("max", Union[int, NoneType]),
+        ]
     )
 
     # Note: Do not use this class attribute.
@@ -184,7 +190,7 @@ def active_preprocessors(self):
                 self.preprocessors is not type(self).preprocessors):
             yield from type(self).preprocessors
 
-    def fitted_parameters(self, *args, **kwargs) -> List:
+    def fitted_parameters(self, *args, **kwargs) -> list:
         return []
 
     # pylint: disable=no-self-use
@@ -891,5 +897,5 @@ def __init__(self, preprocessors=None, **kwargs):
         self.params = kwargs
 
     @SklLearner.params.setter
-    def params(self, values: Dict):
+    def params(self, values: dict):
         self._params = values

Orange/classification/random_forest.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import sklearn.ensemble as skl_ensemble
 
 from Orange.base import RandomForestModel, Learner
@@ -61,6 +59,6 @@ def __init__(self,
         super().__init__(preprocessors=preprocessors)
         self.params = vars()
 
-    def fitted_parameters(self) -> List[Learner.FittedParameter]:
+    def fitted_parameters(self) -> list[Learner.FittedParameter]:
         return [self.FittedParameter("n_estimators", "Number of trees",
                                      "Trees", int, 1, None)]

Orange/modelling/randomforest.py

@@ -1,4 +1,4 @@
-from typing import List, Union
+from typing import Union
 
 from Orange.base import RandomForestModel, Learner
 from Orange.classification import RandomForestLearner as RFClassification
@@ -30,5 +30,5 @@ class RandomForestLearner(SklFitter, _FeatureScorerMixin):
     def fitted_parameters(
             self,
             problem_type: Union[str, Table, Domain]
-    ) -> List[Learner.FittedParameter]:
+    ) -> list[Learner.FittedParameter]:
         return self.get_learner(problem_type).fitted_parameters()

Orange/regression/pls.py

@@ -1,5 +1,3 @@
-from typing import Tuple, List
-
 import numpy as np
 import scipy.stats as ss
 import sklearn.cross_decomposition as skl_pls
@@ -164,11 +162,11 @@ def coefficients_table(self):
         return coef_table
 
     @property
-    def rotations(self) -> Tuple[np.ndarray, np.ndarray]:
+    def rotations(self) -> tuple[np.ndarray, np.ndarray]:
         return self.skl_model.x_rotations_, self.skl_model.y_rotations_
 
     @property
-    def loadings(self) -> Tuple[np.ndarray, np.ndarray]:
+    def loadings(self) -> tuple[np.ndarray, np.ndarray]:
         return self.skl_model.x_loadings_, self.skl_model.y_loadings_
 
     def residuals_normal_probability(self, data: Table) -> Table:
@@ -257,7 +255,7 @@ def incompatibility_reason(self, domain):
                     reason = "Only numeric target variables expected."
         return reason
 
-    def fitted_parameters(self) -> List[Learner.FittedParameter]:
+    def fitted_parameters(self) -> list[Learner.FittedParameter]:
         return [self.FittedParameter("n_components", "Number of components",
                                      "Comp", int, 1, None)]
 

Orange/regression/random_forest.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import sklearn.ensemble as skl_ensemble
 
 from Orange.base import RandomForestModel, Learner
@@ -60,6 +58,6 @@ def __init__(self,
         super().__init__(preprocessors=preprocessors)
         self.params = vars()
 
-    def fitted_parameters(self) -> List[Learner.FittedParameter]:
+    def fitted_parameters(self) -> list[Learner.FittedParameter]:
         return [self.FittedParameter("n_estimators", "Number of trees",
                                      "Trees", int, 1, None)]

Orange/widgets/evaluate/owparameterfitter.py

@@ -1,4 +1,4 @@
-from typing import Optional, Tuple, Callable, List, Dict, Iterable, Sized
+from typing import Optional, Callable, Iterable, Sized
 
 import numpy as np
 from AnyQt.QtCore import QPointF, Qt
@@ -13,7 +13,6 @@
 
 from Orange.base import Learner
 from Orange.data import Table
-from Orange.data.table import DomainTransformationError
 from Orange.evaluation import CrossValidation, TestOnTrainingData, Results
 from Orange.evaluation.scoring import Score, AUC, R2
 from Orange.modelling import Fitter
@@ -32,16 +31,16 @@
 
 N_FOLD = 7
 MIN_MAX_SPIN = 100000
-ScoreType = Tuple[int, Tuple[float, float]]
+ScoreType = tuple[int, tuple[float, float]]
 # scores, score name, tick label
-FitterResults = Tuple[List[ScoreType], str, str]
+FitterResults = tuple[list[ScoreType], str, str]
 
 
 def _validate(
         data: Table,
         learner: Learner,
         scorer: type[Score]
-) -> Tuple[float, float]:
+) -> tuple[float, float]:
     # dummy call - Validation would silence the exceptions
     learner(data)
 
@@ -55,18 +54,18 @@ def _search(
         data: Table,
         learner: Learner,
         fitted_parameter_props: Learner.FittedParameter,
-        initial_parameters: Dict,
+        initial_parameters: dict,
         steps: Sized,
         progress_callback: Callable = dummy_callback
 ) -> FitterResults:
     progress_callback(0, "Calculating...")
     scores = []
     scorer = AUC if data.domain.has_discrete_class else R2
-    parameter_name = fitted_parameter_props.parameter_name
+    name = fitted_parameter_props.name
     for i, value in enumerate(steps):
         progress_callback(i / len(steps))
         params = initial_parameters.copy()
-        params[parameter_name] = value
+        params[name] = value
         result = _validate(data, type(learner)(**params), scorer)
         scores.append((value, result))
     return scores, scorer.name, fitted_parameter_props.tick_label
@@ -76,7 +75,7 @@ def run(
         data: Table,
         learner: Learner,
         fitted_parameter_props: Learner.FittedParameter,
-        initial_parameters: Dict,
+        initial_parameters: dict,
         steps: Sized,
         state: TaskState
 ) -> FitterResults:
@@ -97,7 +96,7 @@ class ParameterSetter(CommonParameterSetter):
     DEFAULT_ALPHA_GRID, DEFAULT_SHOW_GRID = 80, True
 
     def __init__(self, master):
-        self.grid_settings: Dict = None
+        self.grid_settings: dict = None
         self.master: FitterPlot = master
         super().__init__()
 
@@ -148,7 +147,7 @@ def __init__(self):
         super().__init__(enableMenu=False)
         self.__bar_item_tr: pg.BarGraphItem = None
         self.__bar_item_cv: pg.BarGraphItem = None
-        self.__data: List[ScoreType] = None
+        self.__data: list[ScoreType] = None
         self.legend = self._create_legend()
         self.parameter_setter = ParameterSetter(self)
         self.setMouseEnabled(False, False)
@@ -177,7 +176,7 @@ def clear_all(self):
 
     def set_data(
             self,
-            scores: List[ScoreType],
+            scores: list[ScoreType],
             score_name: str,
             tick_name: str
     ):
@@ -243,8 +242,8 @@ def __get_index_at(self, point: QPointF) -> Optional[int]:
         x = point.x()
         index = round(x)
         # pylint: disable=unsubscriptable-object
-        heights_tr: List = self.__bar_item_tr.opts["height"]
-        heights_cv: List = self.__bar_item_cv.opts["height"]
+        heights_tr: list = self.__bar_item_tr.opts["height"]
+        heights_cv: list = self.__bar_item_cv.opts["height"]
         if 0 <= index < len(heights_tr) and abs(index - x) <= self.BAR_WIDTH:
             if index > x and 0 <= point.y() <= heights_tr[index]:
                 return index
@@ -356,15 +355,15 @@ def __on_setting_changed(self):
         self.commit.deferred()
 
     @property
-    def fitted_parameters(self) -> List:
+    def fitted_parameters(self) -> list:
         if not self._learner or not self._data:
             return []
         return self._learner.fitted_parameters(self._data) \
             if isinstance(self._learner, Fitter) \
             else self._learner.fitted_parameters()
 
     @property
-    def initial_parameters(self) -> Dict:
+    def initial_parameters(self) -> dict:
         if not self._learner or not self._data:
             return {}
         return self._learner.get_params(self._data) \
@@ -444,15 +443,15 @@ def _set_range_controls(self):
         else:
             self.__spin_min.setMinimum(-MIN_MAX_SPIN)
             self.__spin_max.setMinimum(-MIN_MAX_SPIN)
-            self.minimum = self.initial_parameters[param.parameter_name]
+            self.minimum = self.initial_parameters[param.name]
         if param.max is not None:
             self.__spin_min.setMaximum(param.max)
             self.__spin_max.setMaximum(param.max)
             self.maximum = param.max
         else:
             self.__spin_min.setMaximum(MIN_MAX_SPIN)
             self.__spin_max.setMaximum(MIN_MAX_SPIN)
-            self.maximum = self.initial_parameters[param.parameter_name]
+            self.maximum = self.initial_parameters[param.name]
 
     def _update_preview(self):
         self.preview = str(list(self.steps))