Handle mypy errors

machine_learning/multinomial_naive_bayes_classifier.py

@@ -11,14 +11,12 @@ https://en.wikipedia.org/wiki/Naive_Bayes_classifier
 import doctest
 
 import numpy as np
-import numpy.typing as npt
-from scipy import sparse
 from sklearn.datasets import fetch_20newsgroups
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics import accuracy_score
 
 
-def group_indices_by_target(targets: npt.ArrayLike) -> dict:
+def group_indices_by_target(targets):
     """
     Associates to each target label the indices of the examples with that label
 
@@ -48,13 +46,13 @@ def group_indices_by_target(targets: npt.ArrayLike) -> dict:
 
 
 class MultinomialNBClassifier:
-    def __init__(self, alpha: int = 1):
+    def __init__(self, alpha=1):
         self.classes = None
         self.features_probs = None
         self.priors = None
         self.alpha = alpha
 
-    def fit(self, data: sparse.csr_matrix, targets: npt.ArrayLike) -> None:
+    def fit(self, data, targets):
         """
         Parameters
         ----------
@@ -81,7 +79,7 @@ class MultinomialNBClassifier:
                     tot_features_count + self.alpha * n_features
                 )
 
-    def predict(self, data: sparse.csr_matrix) -> np.ndarray:
+    def predict(self, data):
         """
         Parameters
         ----------
@@ -97,6 +95,7 @@ class MultinomialNBClassifier:
         ----------
         Let's test the function following an example taken from the documentation
         of the MultinomialNB model from sklearn
+        >>> from scipy import sparse
         >>> rng = np.random.RandomState(1)
         >>> data = rng.randint(5, size=(6, 100))
         >>> data = sparse.csr_matrix(data)
@@ -119,7 +118,7 @@ class MultinomialNBClassifier:
         return np.array(y_pred)
 
 
-def main() -> None:
+def main():
     newsgroups_train = fetch_20newsgroups(subset="train")
     newsgroups_test = fetch_20newsgroups(subset="test")
     x_train = newsgroups_train["data"]