Im gensims latest version, loading trained vectors from a file is done using KeyedVectors, and dosent requires instantiating a new Word2Vec object. But now my code is broken because I can't use the model.vector_size property. What is the alternative to that? I mean something better than just kv[kv.index2word[0]].size.
kv.vector_size still works; I'm using gensim 2.3.0, which is the latest as I write. (I am assuming kv is your KeyedVectors object.) It appears object properties are not documented on the API page, but auto-complete suggests it, and there is no deprecated warning or anything.
Your question helped me answer my own, which was how to get the number of words: len(kv.index2word)
Related
I have been looking into/debugging code transformation related issues in Jest for the last day and a recurring theme is that the SyncTransformer#createTransformer method is a constant source of surprise and it is not really documented why it exists.
The SyncTransformer interface only has a single field one has to implement: process. But it seems that if one implements createTransformer those other methods will not be used: instead Jest seems to create a new transformer using createTransformer, which caused me to lose a few hairs until I figured what was going on. This behaviour is not documented either.
The babel-jest source for Jest 27.
I filed a documentation bug issue with Jest after I could see that the behavior was not mirrored in the docs, which I subsequently fixed by updating the types and docs for code transformation.
The rules for this is basically like follows:
if createTransformer exists as an export, then jest-transform will use that to create a transformer dynamically and not use any of the other exports
if the transformer is imported using import (and not require) it will try to use processAsync and fall back to process if the async version does not exist
Here is some python code that illustrates the problem:
from pyscipopt import Model
master = Model("master LP")
relax = master.relax()
This generates the error:
builtins.AttributeError: 'pyscipopt.scip.Model' object has no attribute 'relax'
These python statements are taken from SCIP documentation --- section Column generation method for the cutting stock problem.
Note, I am using Python 3.6.5 and pyscipopt 3.0.2
The relax method does not exist in PySCIPOpt.
What you link to is a book that, I think, was originally written for using Gurobi's python interface. The authors started translating it to use it with SCIP but this is not ready yet. Actually, you can even see it in the Todo at the beginning of the page you link to.
In any case, this is not the documentation of SCIP
Check the pyscipopt github page and if you have further problems/questions, please open an issue there
I'm quite new to Python so bear with me, I downloaded a github project for Document Forgery Detection that works with 3.6 and buried within it is scipy.misc.imsave which is deprecated.
The call to scipy.misc.imsave only has one parameter but I can't find documentation for that call, only for two parameter methods.
So when I tried to upgrade to what I found recommended online, imageio I replaced the calls as so.
Before
scipy.misc.imsave(self.imageOutputDirectory + "MarkedImage")
After
imageio.imwrite(self.imageOutputDirectory + "MarkedImage")
Of course this doesn't work as it expects two params. The problem is I can't figure out what the original implementation was somehow passing to scipy to reference it, is it some inherited method in the class?
I am trying to use Spark LuceneRDD with Record Linkage concept from the link.
I did all the steps mentioned in the link but I am getting the error
Error: No implicit view available for String => org.apache.lucene.document.Document
I tried by adding lucene jar for spark shell but I am still getting the same error.
Any help is appreciated.
Adding Lucene jars will not help you here. The problem is, that some functionality is using implicit features of Scala. What it means, it should be some mapping function that will transform String into Lucene document.
When I looked over github, I found one implicit thing that will do the conversion - https://github.com/zouzias/spark-lucenerdd/blob/master/src/main/scala/org/zouzias/spark/lucenerdd/package.scala
So, you just need to add import to your code, something like this:
import org.zouzias.spark.lucenerdd._
or even more precisely, if you just need only 1 conversation (could be not your case however)
import org.zouzias.spark.lucenerdd.stringToDocument
I have recently upgraded to the latest version of Stanford CoreNLP. The code I previously used to get the subject or object in a sentence was
System.out.println("subject: "+dependencies.getChildWithReln(dependencies.getFirstRoot(), EnglishGrammaticalRelations.NOMINAL_SUBJECT));
but this now returns null.
I have tried creating a relation with
GrammaticalRelation subjreln =
edu.stanford.nlp.trees.GrammaticalRelation.valueOf("nsubj");
without success. If I extract a relation using code like
GrammaticalRelation target = (dependencies.childRelns(dependencies.getFirstRoot())).iterator().next();
Then run the same request,
System.out.println("target: "+dependencies.getChildWithReln(dependencies.getFirstRoot(), target));
then I get the desired result, confirming that the parsing worked fine (I also know this from printing out the full dependencies).
I suspect my problem has to do with the switch to universal dependencies, but I don't know how to create the GrammaticalRelation from scratch in a way that will match what the dependency parser found.
Since version 3.5.2 the default dependency representation in CoreNLP is Universal Dependencies. This new representation is implemented in a different class (UniversalEnglishGrammaticalRelations) so the GrammaticalStructure objects are now defined somewhere else.
All you have to do to use the new version is to replace EnglishGrammaticalRelations with UniversalGrammaticalRelations:
System.out.println("subject: "+dependencies.getChildWithReln(dependencies.getFirstRoot(), UniversalEnglishGrammaticalRelations.NOMINAL_SUBJECT));
Note, however, that some relations in the new representation are different and might no longer exist (nsubj still does). We are currently compiling migration guidelines from the old representation to the new Universal Dependencies relations. It is still incomplete but it already contains all relation names and their class names in CoreNLP.