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Fixed imports

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This commit is contained in:
ricca 2023-10-06 17:13:04 +02:00
parent 40c39a81f6
commit f6404ccb10
1 changed files with 10 additions and 16 deletions
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26
machine_learning/multinomial_naive_bayes_classifier.py
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@ -9,17 +9,16 @@ https://en.wikipedia.org/wiki/Naive_Bayes_classifier
"""
import doctest
import numpy as np
from numpy.typing import ArrayLike
import numpy.typing as npt
from scipy import sparse
from sklearn.feature_extraction.text import TfidfVectorizer
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: ArrayLike) -> dict:
def group_indices_by_target(targets: npt.ArrayLike) -> dict:
"""
Associates to each target label the indices of the examples with that label
@ -49,24 +48,24 @@ def group_indices_by_target(targets: ArrayLike) -> dict:
class MultinomialNBClassifier:
def __init__(self, alpha=1):
def __init__(self, alpha: int = 1):
self.classes = None
self.features_probs = None
self.priors = None
self.alpha = alpha
def fit(self, data: sparse.csr_matrix, y: ArrayLike) -> None:
def fit(self, data: sparse.csr_matrix, targets: npt.ArrayLike) -> None:
"""
Parameters
----------
data : scipy.sparse.csr_matrix of shape (n_samples, n_features)
Multinomial training examples
y : array-like of shape (n_samples,)
targets : array-like of shape (n_samples,)
Target labels
"""
n_examples, n_features = data.shape
grouped_indices = group_indices_by_target(y)
grouped_indices = group_indices_by_target(targets)
self.classes = list(grouped_indices.keys())
self.priors = np.zeros(shape=len(self.classes))
self.features_probs = np.zeros(shape=(len(self.classes), n_features))
@ -76,15 +75,13 @@ class MultinomialNBClassifier:
prior_class_i = data_class_i.shape[0] / n_examples
self.priors[i] = prior_class_i
tot_features_count = data_class_i.sum() # count of all features in class_i
features_count = np.array(data_class_i.sum(axis=0))[
0
] # count of each feature x_j in class_i
features_count = np.array(data_class_i.sum(axis=0))[0]
for j, n_j in enumerate(features_count):
self.features_probs[i][j] = (self.alpha + n_j) / (
tot_features_count + self.alpha * n_features
)
def predict(self, data: sparse.csr_matrix) -> np.array:
def predict(self, data: sparse.csr_matrix) -> np.ndarray:
"""
Parameters
----------
@ -123,9 +120,6 @@ class MultinomialNBClassifier:
def main() -> None:
"""
Performs the text classification on the twenty_newsgroup dataset from sklearn
"""
newsgroups_train = fetch_20newsgroups(subset="train")
newsgroups_test = fetch_20newsgroups(subset="test")
x_train = newsgroups_train["data"]