Add multinomial Naive Bayes text classification example (#14665)

* Add multinomial Naive Bayes text classification example

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* Address review feedback for Naive Bayes text Classifier

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Raghav8690

machine_learning/naive_bayes_text_classification.py

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+"""
+Naive Bayes text classification using a multinomial event model.
+
+The implementation in this module is intentionally educational and keeps the
+logic explicit: token counting, prior probabilities, and posterior scoring in
+log-space.
+
+References:
+- https://en.wikipedia.org/wiki/Naive_Bayes_classifier
+- https://scikit-learn.org/stable/modules/naive_bayes.html
+"""
+
+from __future__ import annotations
+
+import re
+from collections import Counter, defaultdict
+from math import exp, log
+
+
+class NaiveBayesTextClassifier:
+    """
+    Multinomial Naive Bayes classifier for short text documents.
+
+    Args:
+        alpha: Additive (Laplace) smoothing parameter. Must be greater than 0.
+
+    >>> NaiveBayesTextClassifier(alpha=0)
+    Traceback (most recent call last):
+    ...
+    ValueError: alpha must be greater than 0.
+    """
+
+    def __init__(self, alpha: float = 1.0) -> None:
+        if alpha <= 0:
+            raise ValueError("alpha must be greater than 0.")
+
+        self.alpha = alpha
+        self.classes_: list[str] = []
+        self.vocabulary_: set[str] = set()
+        self.class_document_counts_: Counter[str] = Counter()
+        self.class_token_counts_: dict[str, Counter[str]] = defaultdict(Counter)
+        self.class_total_tokens_: Counter[str] = Counter()
+        self.class_log_prior_: dict[str, float] = {}
+        self.is_fitted_ = False
+
+    @staticmethod
+    def _tokenize(text: str) -> list[str]:
+        """
+        Split text into lowercase alphanumeric tokens.
+
+        >>> NaiveBayesTextClassifier._tokenize("Hello, NLP world!")
+        ['hello', 'nlp', 'world']
+        """
+        return re.findall(r"[a-z0-9']+", text.lower())
+
+    def fit(self, texts: list[str], labels: list[str]) -> None:
+        """
+        Fit the classifier from labeled training texts.
+
+        >>> model = NaiveBayesTextClassifier()
+        >>> model.fit(["cheap meds", "project meeting"], ["spam", "ham"])
+        >>> sorted(model.classes_)
+        ['ham', 'spam']
+
+        >>> model.fit(["only one text"], ["ham", "spam"])
+        Traceback (most recent call last):
+        ...
+        ValueError: texts and labels must have the same length.
+
+        >>> model.fit([], [])
+        Traceback (most recent call last):
+        ...
+        ValueError: training data must not be empty.
+        """
+        if not texts:
+            raise ValueError("training data must not be empty.")
+        if len(texts) != len(labels):
+            raise ValueError("texts and labels must have the same length.")
+
+        self.classes_ = sorted(set(labels))
+        self.vocabulary_.clear()
+        self.class_document_counts_.clear()
+        self.class_token_counts_ = defaultdict(Counter)
+        self.class_total_tokens_.clear()
+        self.class_log_prior_.clear()
+
+        for text, label in zip(texts, labels):
+            if not isinstance(text, str) or not isinstance(label, str):
+                raise TypeError("texts and labels must contain strings only.")
+
+            tokens = self._tokenize(text)
+            self.class_document_counts_[label] += 1
+            self.class_token_counts_[label].update(tokens)
+            self.class_total_tokens_[label] += len(tokens)
+            self.vocabulary_.update(tokens)
+
+        total_documents = len(texts)
+        self.class_log_prior_ = {
+            label: log(self.class_document_counts_[label] / total_documents)
+            for label in self.classes_
+        }
+        self.is_fitted_ = True
+
+    def predict_proba(self, text: str) -> dict[str, float]:
+        """
+        Return posterior probabilities for every class.
+
+        >>> train_texts, train_labels = build_toy_dataset()
+        >>> model = NaiveBayesTextClassifier()
+        >>> model.fit(train_texts, train_labels)
+        >>> probs = model.predict_proba("cheap meds available now")
+        >>> round(sum(probs.values()), 6)
+        1.0
+        >>> probs['spam'] > probs['ham']
+        True
+
+        An empty input text has no tokens, so predictions fall back to class priors.
+        >>> empty_probs = model.predict_proba("")
+        >>> round(empty_probs['spam'], 3), round(empty_probs['ham'], 3)
+        (0.5, 0.5)
+
+        >>> NaiveBayesTextClassifier().predict_proba("hello")
+        Traceback (most recent call last):
+        ...
+        ValueError: model has not been fitted yet.
+        """
+        if not self.is_fitted_:
+            raise ValueError("model has not been fitted yet.")
+        if not isinstance(text, str):
+            raise TypeError("text must be a string.")
+
+        tokens = self._tokenize(text)
+        vocabulary_size = len(self.vocabulary_)
+        log_posteriors: dict[str, float] = {}
+
+        for label in self.classes_:
+            log_prob = self.class_log_prior_[label]
+            token_counts = self.class_token_counts_[label]
+            denominator = self.class_total_tokens_[label] + self.alpha * vocabulary_size
+
+            for token in tokens:
+                count = token_counts[token]
+                log_prob += log((count + self.alpha) / denominator)
+
+            log_posteriors[label] = log_prob
+
+        max_log = max(log_posteriors.values())
+        exp_scores = {
+            label: exp(score - max_log) for label, score in log_posteriors.items()
+        }
+        normalizer = sum(exp_scores.values())
+        return {label: score / normalizer for label, score in exp_scores.items()}
+
+    def predict(self, text: str) -> str:
+        """
+        Predict the most likely class label for a text.
+
+        >>> train_texts, train_labels = build_toy_dataset()
+        >>> model = NaiveBayesTextClassifier(alpha=1.0)
+        >>> model.fit(train_texts, train_labels)
+        >>> model.predict("free cheap meds")
+        'spam'
+        >>> model.predict("project meeting schedule")
+        'ham'
+        """
+        probabilities = self.predict_proba(text)
+        return max(probabilities, key=lambda label: probabilities[label])
+
+
+def build_toy_dataset() -> tuple[list[str], list[str]]:
+    """
+    Build a tiny text dataset for examples and quick local testing.
+
+    >>> texts, labels = build_toy_dataset()
+    >>> len(texts), len(labels)
+    (6, 6)
+    >>> sorted(set(labels))
+    ['ham', 'spam']
+    """
+    texts = [
+        "buy cheap meds now",
+        "cheap meds available online",
+        "win cash prizes now",
+        "project meeting schedule attached",
+        "let us discuss the project timeline",
+        "team meeting moved to monday",
+    ]
+    labels = ["spam", "spam", "spam", "ham", "ham", "ham"]
+    return texts, labels
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+
+    sample_texts, sample_labels = build_toy_dataset()
+    classifier = NaiveBayesTextClassifier(alpha=1.0)
+    classifier.fit(sample_texts, sample_labels)
+
+    print("Prediction:", classifier.predict("cheap prizes available now"))
+    print("Prediction:", classifier.predict("team meeting about project timeline"))