I've been working on a predictive model with a dataset that's been pre-split into training, validation, and test set already.
I understand that I am supposed to do any data-processing after the split, but I am not sure whether I need to apply my data-processing (Knn-Imputation and SMOTE-balancing) on the training set exclusively or also on the test set.
Thank you for your help :))
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I am exploring the possibility of implementing Featuretools into my pipeline, to be able to create new features from my Df.
Currently I am using a GridSearchCV, with a Pipeline embedded inside it. Since Featuretools is creating new features with aggregation on columns, like STD(column) etc, I feel like it is suspectible to data leakage. In their FAQ, they are giving an example approach to tackle it, which is not suitable for a Pipeline structure I am using.
Idea 0: I would love to integrate it directly into my Pipeline but it seems like not compatible with Pipelines. It would use fold train data to construct features, transform fold test data. K times. At the end, it would use whole data to construct, during Refit= True stage of GridSearchCV. If you have any example opposed to this fact, you are very welcome.
Idea 1: I can switch to a manual CV structure, not embedded into pipeline. And inside it, I can use Train data to construct new features, and test data to transform with these. It will work K times. At the end, all data can be used to construct Ultimate model.
It is the safest option, with time and complexity disadvantages.
Idea 2: Using it with whole data, ignore the leakage possibility. I am not in favor of this of course. But when I look at Project Github page, all the examples are combining Train and Test data, creating these features with whole data. Then go on with Train-Test division for modeling.
https://github.com/Featuretools/predict-taxi-trip-duration/blob/master/NYC%20Taxi%203%20-%20Simple%20Featuretools.ipynb
Actually if the developers of the project think like that, I could give it a chance with whole data.
What do you think, I would love to hear about your experiences on FeatureTools.
I've generated a PySpark Word2Vec model like so:
from pyspark.ml.feature import Word2Vec
w2v = Word2Vec(vectorSize=100, minCount=1, inputCol='words', outputCol = 'vector')
model = w2v.fit(df)
(The data that I used to train the model on isn't relevant, what's important is that its all in the right format and successfully yields a pyspark.ml.feature.Word2VecModel object.)
Now I need to convert this model to a Gensim Word2Vec model. How would I go about this?
If you still have the training data, re-training the gensim Word2Vec model may be the most straightforward approach.
If you only need the word-vectors, perhaps PySpark's model can export them in the word2vec.c format that gensim can load with .load_word2vec_format().
The only reason to port the model would be to continue training. Such incremental training, while possible, involves considering a lot of tradeoffs in balancing the influence of the older and later training to get good results.
If you are in fact wanting to do this conversion in order to do more training in such a manner, it again suggests that using the original training to reproduce a similar model could be plausible.
But, if you have to convert the model, the general approach would be to study the source code and internal data structures of the two models, to discover how they alternatively represent each of the key aspects of the model:
the known word-vectors (model.wv.vectors in gensim)
the known-vocabulary of words, including stats about word-frequencies and the position of individual words (model.wv.vocab in gensim)
the hidden-to-output weights of the model (`model.trainables' and its properties in gensim)
other model properties describing the model's modes & metaparameters
A reasonable interactive approach could be:
Write some acceptance tests that take models of both types, and test whether they are truly 'equivalent' for your purposes. (This is relatively easy for just checking if the vectors for individual words are present and identical, but nearly as hard as the conversion itself for verifying other ready-to-be-trained-more behaviors.)
Then, in an interactive notebook, load the source model, and also create a dummy gensim model with the same vocabulary size. Consulting the source code, write Python statements to iteratively copy/transform key properties over from the source into the target, repeatedly testing if they verify as equivalent.
When they do, take those steps you did manually and combine them into a utility method to do the conversion. Again verify its operation then try using the converted model however you'd hoped – perhaps discovering overlooked info or discovering other bugs in the process, and then improving the verification method and conversion method.
It's possible that the PySpark model will be missing things the gensim model expects, which might require synthesizing workable replacement values.
Good luck! (But re-train the gensim model from the original data if you want things to just be straightforward and work.)
I try to create the easiest of a NeuralNetwork and training it with some data:
Therefore I created a test.csv with a the following pattern:
number,number+1;
number2,number2+1
...
I try to make a linear regression with the network...
But I do not find a way to acquire the data, DataSetIterator does not work.
How to fit the Data, how to test the Data?
In our examples, we encourage people to use datavec + recordreaderdatasetiterator.
Datavec has all of the various data loading components.
I'm not sure what you mean about "datasetiterator not working" wihtout seeing any code, but it seems like you didn't really look at our examples.
In there are multiple examples of a csv record reader you can use for both regression and classification use cases.
Consider reorienting your data pipeline to use those.
Those examples are always found here:
https://github.com/deeplearning4j/dl4j-examples
If you follow any of those, the same pattern emerges:
Record reader for whatever data format -> RecordReaderDataSetIterator
The iterator allows you to specify common constructors such as whether it is a regression or not, which column your label is etc.
How can I use RapidMiner to run the classifier on a test data, and classify a user based on that data - I need it to actually output who the classified user is, and not its performance. Any help would be greatly appreciated.
I found the answer to my question!
You just have to use an example (row) with Attributes(Column Headers) and then feed it to the Apply Model operator. Make sure you remove the label(or what you want to be predicted) from that example.
The results will give you a row with an added attribute called Prediction.
Gensim's official tutorial explicitly states that it is possible to continue training a (loaded) model. I'm aware that according to the documentation it is not possible to continue training a model that was loaded from the word2vec format. But even when one generates a model from scratch and then tries to call the train method, it is not possible to access the newly created labels for the LabeledSentence instances supplied to train.
>>> sentences = [LabeledSentence(['first', 'sentence'], ['SENT_0']), LabeledSentence(['second', 'sentence'], ['SENT_1'])]
>>> model = Doc2Vec(sentences, min_count=1)
>>> print(model.vocab.keys())
dict_keys(['SENT_0', 'SENT_1', 'sentence', 'first', 'second'])
>>> sentence = LabeledSentence(['third', 'sentence'], ['SENT_2'])
>>> model.train([sentence])
>>> print(model.vocab.keys())
# At this point I would expect the key 'SENT_2' to be present in the vocabulary, but it isn't
dict_keys(['SENT_0', 'SENT_1', 'sentence', 'first', 'second'])
Is it at all possible to continue the training of a Doc2Vec model in Gensim with new sentences? If so, how can this be achieved?
My understand is that this is not possible for any new labels. We can only continue training when the new data has the same labels as the old data. As a result, we are training or retuning the weights of the already learned vocabulary, but are not able to learn a new vocabulary.
There is a similar question for adding new labels/words/sentences during training: https://groups.google.com/forum/#!searchin/word2vec-toolkit/online$20word2vec/word2vec-toolkit/L9zoczopPUQ/_Zmy57TzxUQJ
Also, you might want to keep an eye on this discussion:
https://groups.google.com/forum/#!topic/gensim/UZDkfKwe9VI
Update: If you want to add new words to an already trained model, take a look at online word2vec here:
http://rutumulkar.com/blog/2015/word2vec/
According to gensim documentation online/incremental training is not supported for doc2vec.
refer to https://github.com/RaRe-Technologies/gensim/issues/1019
I could still add new documents to an existing doc2vec model( but some it crashes due to segmentation fault) but most similar query does not work on newly added document(so this approach seems useless).