ML之NB：利用朴素贝叶斯NB算法(CountVectorizer+不去除停用词)对fetch_20newsgroups数据集(20类新闻文本)进行分类预测、评估

ML之NB：利用朴素贝叶斯NB算法(CountVectorizer+不去除停用词)对fetch_20newsgroups数据集(20类新闻文本)进行分类预测、评估

---

输出结果

设计思路

核心代码

https://www.cnblogs.com/yunyaniu/articles/10465701.html

class MultinomialNB Found at: sklearn.naive_bayes

class MultinomialNB(BaseDiscreteNB):
    """
    Naive Bayes classifier for multinomial models

    The multinomial Naive Bayes classifier is suitable for classification with
    discrete features (e.g., word counts for text classification). The
    multinomial distribution normally requires integer feature counts. However,
    in practice, fractional counts such as tf-idf may also work.

    Read more in the :ref:`User Guide <multinomial_naive_bayes>`.

    Parameters
    ----------
    alpha : float, optional (default=1.0)
    Additive (Laplace/Lidstone) smoothing parameter
    (0 for no smoothing).

    fit_prior : boolean, optional (default=True)
    Whether to learn class prior probabilities or not.
    If false, a uniform prior will be used.

    class_prior : array-like, size (n_classes,), optional (default=None)
    Prior probabilities of the classes. If specified the priors are not
    adjusted according to the data.

    Attributes
    ----------
    class_log_prior_ : array, shape (n_classes, )
    Smoothed empirical log probability for each class.

    intercept_ : property
    Mirrors ``class_log_prior_`` for interpreting MultinomialNB
    as a linear model.

    feature_log_prob_ : array, shape (n_classes, n_features)
    Empirical log probability of features
    given a class, ``P(x_i|y)``.

    coef_ : property
    Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
    as a linear model.

    class_count_ : array, shape (n_classes,)
    Number of samples encountered for each class during fitting. This
    value is weighted by the sample weight when provided.

    feature_count_ : array, shape (n_classes, n_features)
    Number of samples encountered for each (class, feature)
    during fitting. This value is weighted by the sample weight when
    provided.

    Examples
    --------
    >>> import numpy as np
    >>> X = np.random.randint(5, size=(6, 100))
    >>> y = np.array([1, 2, 3, 4, 5, 6])
    >>> from sklearn.naive_bayes import MultinomialNB
    >>> clf = MultinomialNB()
    >>> clf.fit(X, y)
    MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)
    >>> print(clf.predict(X[2:3]))
    [3]

    Notes
    -----
    For the rationale behind the names `coef_` and `intercept_`, i.e.
    naive Bayes as a linear classifier, see J. Rennie et al. (2003),
    Tackling the poor assumptions of naive Bayes text classifiers, ICML.

    References
    ----------
    C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
    Information Retrieval. Cambridge University Press, pp. 234-265.
    http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-
     classification-1.html
    """
    def __init__(self, alpha=1.0, fit_prior=True, class_prior=None):
        self.alpha = alpha
        self.fit_prior = fit_prior
        self.class_prior = class_prior

    def _count(self, X, Y):
        """Count and smooth feature occurrences."""
        if np.any((X.data if issparse(X) else X) < 0):
            raise ValueError("Input X must be non-negative")
        self.feature_count_ += safe_sparse_dot(Y.T, X)
        self.class_count_ += Y.sum(axis=0)

    def _update_feature_log_prob(self, alpha):
        """Apply smoothing to raw counts and recompute log probabilities"""
        smoothed_fc = self.feature_count_ + alpha
        smoothed_cc = smoothed_fc.sum(axis=1)
        self.feature_log_prob_ = np.log(smoothed_fc) - np.log(smoothed_cc.
         reshape(-1, 1))

    def _joint_log_likelihood(self, X):
        """Calculate the posterior log probability of the samples X"""
        check_is_fitted(self, "classes_")
        X = check_array(X, accept_sparse='csr')
        return safe_sparse_dot(X, self.feature_log_prob_.T) + self.class_log_prior_