[WIP] Added sklearn wrapper for LDASeq model #1405
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| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz> | ||
| # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html | ||
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| """ | ||
| Scikit learn interface for gensim for easy use of gensim with scikit-learn | ||
| Follows scikit-learn API conventions | ||
| """ | ||
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| import numpy as np | ||
| from sklearn.base import TransformerMixin, BaseEstimator | ||
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| from gensim import models | ||
| from gensim.sklearn_integration import base_sklearn_wrapper | ||
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| class SklLdaSeqModel(base_sklearn_wrapper.BaseSklearnWrapper, TransformerMixin, BaseEstimator): | ||
| """ | ||
| Base LdaSeq module | ||
| """ | ||
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| def __init__(self, time_slice=None, id2word=None, alphas=0.01, num_topics=10, | ||
| initialize='gensim', sstats=None, lda_model=None, obs_variance=0.5, chain_variance=0.005, passes=10, | ||
| random_state=None, lda_inference_max_iter=25, em_min_iter=6, em_max_iter=20, chunksize=100): | ||
| """ | ||
| Sklearn wrapper for LdaSeq model. Class derived from gensim.models.LdaSeqModel | ||
| """ | ||
| self.gensim_model = None | ||
| self.time_slice = time_slice | ||
| self.id2word = id2word | ||
| self.alphas = alphas | ||
| self.num_topics = num_topics | ||
| self.initialize = initialize | ||
| self.sstats = sstats | ||
| self.lda_model = lda_model | ||
| self.obs_variance = obs_variance | ||
| self.chain_variance = chain_variance | ||
| self.passes = passes | ||
| self.random_state = random_state | ||
| self.lda_inference_max_iter = lda_inference_max_iter | ||
| self.em_min_iter = em_min_iter | ||
| self.em_max_iter = em_max_iter | ||
| self.chunksize = chunksize | ||
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| def get_params(self, deep=True): | ||
| """ | ||
| Returns all parameters as dictionary. | ||
| """ | ||
| return {"time_slice": self.time_slice, "id2word": self.id2word, | ||
| "alphas": self.alphas, "num_topics": self.num_topics, "initialize": self.initialize, | ||
| "sstats": self.sstats, "lda_model": self.lda_model, "obs_variance": self.obs_variance, | ||
| "chain_variance": self.chain_variance, "passes": self.passes, "random_state": self.random_state, | ||
| "lda_inference_max_iter": self.lda_inference_max_iter, "em_min_iter": self.em_min_iter, | ||
| "em_max_iter": self.em_max_iter, "chunksize": self.chunksize} | ||
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| def set_params(self, **parameters): | ||
| """ | ||
| Set all parameters. | ||
| """ | ||
| super(SklLdaSeqModel, self).set_params(**parameters) | ||
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| def fit(self, X, y=None): | ||
| """ | ||
| Fit the model according to the given training data. | ||
| Calls gensim.models.LdaSeqModel | ||
| """ | ||
| self.gensim_model = models.LdaSeqModel(corpus=X, time_slice=self.time_slice, id2word=self.id2word, | ||
| alphas=self.alphas, num_topics=self.num_topics, initialize=self.initialize, sstats=self.sstats, | ||
| lda_model=self.lda_model, obs_variance=self.obs_variance, chain_variance=self.chain_variance, | ||
| passes=self.passes, random_state=self.random_state, lda_inference_max_iter=self.lda_inference_max_iter, | ||
| em_min_iter=self.em_min_iter, em_max_iter=self.em_max_iter, chunksize=self.chunksize) | ||
| return self | ||
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| def transform(self, docs): | ||
| """ | ||
| Return the topic proportions for the documents passed. | ||
| """ | ||
| # The input as array of array | ||
| check = lambda x: [x] if isinstance(x[0], tuple) else x | ||
| docs = check(docs) | ||
| X = [[] for _ in range(0, len(docs))] | ||
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| for k, v in enumerate(docs): | ||
| transformed_author = self.gensim_model[v] | ||
| # Everything should be equal in length | ||
| if len(transformed_author) != self.num_topics: | ||
| transformed_author.extend([1e-12] * (self.num_topics - len(transformed_author))) | ||
| X[k] = transformed_author | ||
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| return np.reshape(np.array(X), (len(docs), self.num_topics)) | ||
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| def partial_fit(self, X): | ||
| raise NotImplementedError("'partial_fit' has not been implemented for the LDA Seq model") | ||
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Chek case, when you create instance and call transform immediately (without fit), you need to raise exception like sklearn
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Also, please add an example of
docsparam in docstring.There was a problem hiding this comment.
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@menshikh-iv For checking if the model has been fitted, would it be a good idea to check if
self.gensim_modelisNoneor not? This approach would clearly give an error whenfithasn't been called before callingtransformbut this also allows the user to set the value ofself.gensim_modelthroughset_paramsfunction (or even aswrapper.gensim_model=...) and then calltransformfunction, which makes sense for us to allow.There was a problem hiding this comment.
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I completely forgot about
set_param, so, I think if you disablegensim_modelin set_param, you can checkmodel is None(it does not cover all cases, but covers the most obvious)There was a problem hiding this comment.
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Could you elaborate the meaning of "disabling"
gensim_modelparam from the functionset_params?Actually,
gensim_modelis a public attribute of the model so it can be set likeldaseq_wrapper.gensim_model = some_model, which is almost the same as usingset_paramsfunction to set this value. So, checking whetherself.gensim_modelisNoneshould be enough, right?This would be like :
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Ok, as a temporary option.