Add type annotations to components

miplearn/classifiers/adaptive.py

@@ -4,6 +4,7 @@
 
 import logging
 from copy import deepcopy
+from typing import Any, Dict
 
 from sklearn.linear_model import LogisticRegression
 from sklearn.neighbors import KNeighborsClassifier
@@ -25,9 +26,9 @@ class AdaptiveClassifier(Classifier):
 
     def __init__(
         self,
-        candidates=None,
-        evaluator=ClassifierEvaluator(),
-    ):
+        candidates: Dict[str, Any] = None,
+        evaluator: ClassifierEvaluator = ClassifierEvaluator(),
+    ) -> None:
         """
         Initializes the meta-classifier.
         """

miplearn/classifiers/counting.py

@@ -15,7 +15,7 @@ class CountingClassifier(Classifier):
     counts how many times each label appeared, hence the name.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.mean = None
 
     def fit(self, x_train, y_train):

miplearn/classifiers/evaluator.py

@@ -6,7 +6,7 @@ from sklearn.metrics import roc_auc_score
 
 
 class ClassifierEvaluator:
-    def __init__(self):
+    def __init__(self) -> None:
         pass
 
     def evaluate(self, clf, x_train, y_train):

miplearn/classifiers/threshold.py

@@ -7,10 +7,17 @@ from abc import abstractmethod, ABC
 import numpy as np
 from sklearn.metrics._ranking import _binary_clf_curve
 
+from miplearn.classifiers import Classifier
+
 
 class DynamicThreshold(ABC):
     @abstractmethod
-    def find(self, clf, x_train, y_train):
+    def find(
+        self,
+        clf: Classifier,
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+    ) -> float:
         """
         Given a trained binary classifier `clf` and a training data set,
         returns the numerical threshold (float) satisfying some criterea.
@@ -24,7 +31,7 @@ class MinPrecisionThreshold(DynamicThreshold):
     positive rate (also known as precision).
     """
 
-    def __init__(self, min_precision):
+    def __init__(self, min_precision: float) -> None:
         self.min_precision = min_precision
 
     def find(self, clf, x_train, y_train):