46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
import re
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import numpy as np
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from sklearn.base import BaseEstimator, TransformerMixin
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STOPWORDS = {
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"a", "an", "the", "is", "it", "in", "on", "at", "to", "for",
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"of", "and", "or", "but", "not", "with", "as", "by", "from",
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"this", "that", "was", "are", "be", "been", "have", "has",
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"had", "do", "did", "will", "would", "could", "should",
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"i", "me", "my", "you", "your", "he", "she", "we", "they",
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"his", "her", "our", "their", "its", "what", "which",
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}
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class LowercaseTransformer(BaseEstimator, TransformerMixin):
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def fit(self, X, y=None):
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self.fitted_ = True
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return self
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def transform(self, X):
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return np.array([msg.lower() for msg in X])
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class StopwordRemover(BaseEstimator, TransformerMixin):
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def fit(self, X, y=None):
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self.fitted_ = True
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return self
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def transform(self, X):
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return np.array([self._remove(msg) for msg in X])
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def _remove(self, message):
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words = message.split()
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return " ".join(w for w in words if w.lower() not in STOPWORDS)
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class PunctuationRemover(BaseEstimator, TransformerMixin):
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def fit(self, X, y=None):
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self.fitted_ = True
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return self
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def transform(self, X):
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return np.array([re.sub(r"[^\w\s]", " ", msg) for msg in X])
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