Topic modeling #37
Topic modeling #37
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| ], | ||
| 'topic_modeling': [ | ||
| 'gensim', | ||
| 'spacy' |
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Do we not need a spacy corpus as well?
quantgov/estimator/structures.py
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| class QGLdaModel(BaseEstimator, TransformerMixin): | ||
| @check_gensim | ||
| @check_spacy | ||
| def __init__(self, word_regex=r'\b[A-z]{2,}\b', stop_words=STOP_WORDS): |
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I would think the options for stop_words should be:
None(default): No stop wordsTrue: use built-in stop words- A sequence: user-specified stop words.
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Not having any stop words seems to output a pretty unusable model - my thinking is it's best to have some default, and if the user chooses to override that default with None they can, but the defaults should be able to produce something usable - we could include some output if they don't provide any (e.g. "INFO: No stop words provided, using sklearn builtins"), and potentially a warning if None is passed
quantgov/estimator/structures.py
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| class QGLdaModel(BaseEstimator, TransformerMixin): | ||
| @check_gensim | ||
| @check_spacy | ||
| def __init__(self, word_regex=r'\b[A-z]{2,}\b', stop_words=STOP_WORDS): |
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word_regex should be word_pattern to match what already exists in SKL.
quantgov/estimator/structures.py
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| @@ -85,3 +113,41 @@ class CandidateModel( | |||
| parameter values to test as values | |||
| """ | |||
| pass | |||
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| class QGLdaModel(BaseEstimator, TransformerMixin): | |||
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I don't like either the prefix or the Model specifier. I'd call this GensimLDA or something like that.
quantgov/estimator/structures.py
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| import re | ||
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| try: | ||
| from spacy.lang.en.stop_words import STOP_WORDS |
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If we're literally only using spacy here for the stopwords, can't we somehow find the sklearn stopwords used in the CountVectorizer? That's got to be importable from somewhere.
| for doc in driver.stream()]) | ||
| stop_ids = [self.dictionary.token2id[stopword] for stopword | ||
| in self.stop_words if stopword in self.dictionary.token2id] | ||
| once_ids = [tokenid for tokenid, docfreq in |
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Filtering out words that only occur once was recommended in the Gensim documentation - beyond that, I don't know if it actually improves the performance of the model.
quantgov/estimator/structures.py
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| for i in self.word_regex | ||
| .finditer(doc.text)] | ||
| for doc in driver.stream()]) | ||
| stop_ids = [self.dictionary.token2id[stopword] for stopword |
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Wouldn't it be better to only pass the dictionary words that aren't in stop_words?
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@OliverSherouse ready for follow up review |
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