I'm working on a classificaton problem on Weka. I'm taking am arff file as my training data and taking my test data from database. But they are incompatible. In Weka tool I can use the InputMappedClassifier and get over the problem. But I can't implement it in Java code. Please help. Thanks.
This seem to occur for me when the values are in a different order, e.g. one file says
#attribute class {Iris-versicolor,Iris-virginica}
and the other file says
#attribute class {Iris-virginica, Iris-versicolor}
so you might be able to fix this by converting the header information in the test set to match the training set.
Of course, if there are values in the test set that aren't in the training set, this won't work, but that's a different problem.
Related
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.
I am working on a project in which I have about 18 classes with about 4,000 total instances. I have 7 attributes, 1 being string data, the rest nominal. I am currently using StringToWordVector on the string attribute with Platt's SMO classifier, achieving good results. We are about to implement this, but I would like to try other classifiers in case there maybe one I could get better results from. Any suggestions?
Also, should I be using MultiClassClassifier with so many classes? If so, what settings should I try within that?
Any advice is appreciated!
An AdaBoosted J48 Decision Tree yielded the best results has been well established in our division
Hi all I am new to scala and spark MLIB.
I have a dataset of diseses of diseases along with the symptoms which are in the following format:
Disease,symptom1 symptom2 symptom3
I have almost 300 entries which are in the above mentioned format in a CSV file.
I want to achieve this following functionality:
If a user has given a input of sysmptoms namely Symptom1,Symptom2,Symptom3 the model must be able to predict the disease.
I have the following Questions:
which machine learning model should I use to achieve this functionality.
I have gone through some models and founf NAIVES Bayes model if wrong correct me.
can I provide text input to Naives Bayes model.
Is there any sample code available to achieve this functionality.
You can use any of the classification algorithms present in Spark MLlib for further reference read the official docs and go thru this link from databricks blog https://databricks.com/blog/2015/07/29/new-features-in-machine-learning-pipelines-in-spark-1-4.html