43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
import numpy as np
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import pandas as pd
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from sklearn.base import BaseEstimator
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import Pipeline
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from research.traditional_model import TraditionalModel
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class NaiveBayesModel(TraditionalModel):
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"""Multinomial Naive Bayes with character n-grams"""
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def build_model(self) -> BaseEstimator:
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params = self.config.model_params
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# Bag-of-character-ngrams aligns with Multinomial NB assumptions; (1,4)
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# includes unigrams for coverage and higher n for suffix/prefix cues.
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vectorizer = CountVectorizer(
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analyzer="char",
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ngram_range=params.get("ngram_range", (2, 5)),
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max_features=params.get("max_features", 8000),
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)
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# Laplace smoothing (alpha) counters zero counts for rare n-grams.
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classifier = MultinomialNB(alpha=params.get("alpha", 1.0))
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return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])
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def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
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text_features = []
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for feature_type in self.config.features:
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if feature_type.value in X.columns:
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text_features.append(X[feature_type.value].astype(str))
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if len(text_features) == 1:
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return text_features[0].values
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else:
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combined = text_features[0].astype(str)
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for feature in text_features[1:]:
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combined = combined + " " + feature.astype(str)
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return combined.values
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