something like the following may suffice:
def get_params(self, deep=True):
out = super(WordCooccurrenceVectorizer, self).get_params(deep=deep)
out['w2v_clusters'] = self.w2v_clusters
return out
On 23 March 2016 at 15:01, Joel Nothman <joel.noth...@gmail.com> wrote:
> Hi Fred,
>
> We use the __init__ signature to get the list of parameters that (a) can
> be set by grid search; (b) need to be copied to a cloned instance of the
> estimator (with any fitted model discarded) in constructing ensembles,
> cross validation, etc. While none of the scikit-learn library of estimators
> do this, in practice you can overload get_params to define your own
> parameter listing. See
> http://scikit-learn.org/stable/developers/contributing.html#get-params-and-set-params
>
> On 23 March 2016 at 14:45, Fred Mailhot <fred.mail...@gmail.com> wrote:
>
>> Hello list,
>>
>> Firstly, thanks for this incredible package; I use it daily at work. Now
>> on to the meat: I'm trying to subclass TfidfVectorizer and running into
>> issues. I want to specify an extra param for __init__() that points to a
>> file that gets used in build_analyzer(). Skipping irrelevant bits, I've got
>> the following:
>>
>> #======================
>> class WordCooccurrenceVectorizer(TfidfVectorizer):
>>
>> ### override __init__ to add w2v_clusters arg
>> # see
>> http://stackoverflow.com/questions/2215923/avoid-specifying-all-arguments-in-a-subclass
>> # for explanation of syntax
>> def __init__(self, *args, **kwargs):
>> try:
>> self.w2v_cluster_path = kwargs.pop("w2v_clusters")
>> except KeyError:
>> pass
>> super(WordCooccurrenceVectorizer, self).__init__(*args, **kwargs)
>>
>> def build_analyzer(self):
>> preprocess = self.build_preprocessor()
>> stopwords = self.get_stop_words()
>> w2v_clusters = self.load_w2v_clusters()
>> tokenize = self.build_tokenizer()
>> return lambda doc:
>> self._nwise(tokenize(preprocess(self.decode(doc))), stopwords, w2v_clusters)
>> [...]
>> #======================
>>
>> I can instantiate this, but when I want to inspect it, I get the
>> following (this is in ipython, in a script it just hangs):
>>
>> #======================
>> In [2]: vec = WordCooccurrenceVectorizer(ngram_range=(2,2),
>> stop_words="english", max_df=0.5, min_df=1, max_features=10000,
>> w2v_clusters="clusters.20160322_1803.w2v", binary=True)
>>
>> In [3]: vec
>> Out[3]:
>> ---------------------------------------------------------------------------
>> RuntimeError Traceback (most recent call
>> last)
>> /Users/fredmailhot/anaconda/envs/csai_experiments/lib/python2.7/site-packages/IPython/core/formatters.pyc
>> in __call__(self, obj)
>> 697 type_pprinters=self.type_printers,
>> 698 deferred_pprinters=self.deferred_printers)
>> --> 699 printer.pretty(obj)
>> 700 printer.flush()
>> 701 return stream.getvalue()
>>
>> [...]
>>
>> /Users/fredmailhot/anaconda/envs/csai_experiments/lib/python2.7/site-packages/sklearn/base.pyc
>> in _get_param_names(cls)
>> 193 " %s with constructor %s
>> doesn't "
>> 194 " follow this convention."
>> --> 195 % (cls, init_signature))
>> 196 # Extract and sort argument names excluding 'self'
>> 197 return sorted([p.name for p in parameters])
>>
>> RuntimeError: scikit-learn estimators should always specify their
>> parameters in the signature of their __init__ (no varargs). <class
>> 'cooc_vectorizer.WordCooccurrenceVectorizer'> with constructor (<self>,
>> *args, **kwargs) doesn't follow this convention.
>>
>> In [4]:
>> #======================
>>
>> The error is clear enough -- I can't use *args and **kwargs in a sklearn
>> estimator's __init__() -- but I'm not sure what the correct way is to do
>> what I need to do. Do I literally need to specify all of the __init__
>> params in my subclass and then pass them on to the __init__ of super()? If
>> so, what's the reason for setting this up this way?
>>
>>
>> Thanks for any pointers/guidance,
>> Fred.
>>
>>
>>
>> ------------------------------------------------------------------------------
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>>
>
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