Is it possible to use data stored in Sframe to train e.g., a Random Forest, of scikit-learn implementation without converting the whole dataset to numpy?
According by Turi-forum:
"If you use the most recent version of SFrame (which only became available via pip yesterday) you can use the tonumpy function to create an ndarray from an SFrame."
https://forum.turi.com/discussion/1642/
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I am trying to use dask_cudf to preprocess a very large dataset (150,000,000+ records) for multi-class xgboost training and am having trouble encoding the class column (dtype is string). I tried using the 'replace' function, but the error message said the two dtypes must match. I tried using dask_ml.LabelEncoder, but it said string arrays aren't supported in cudf. I tried using compute() in various ways, but i kept running into out-of-memory errors (i'm assuming because operations on cudf dataframe require a smaller dataset). I also tried pulling the class column out, encoding, and then merging it back with the dataframe, but the partitions do not line up. I tried manually lining them up, but dask_cudf seemingly does not support repartioning using 'divisions' parameter (got error saying something like 'old and new partitions do not match'). Any help on how to do this would be much appreciated.
Strings aren't supported on xgboost. Not having seen your data, here are a few ways quick and dirty ways I've modified string columns to train, as generally strings may not matter:
If the strings were actually numeric (like dates), converting to int (int8 int16, int32)
I did this by hashmapping the strings and then running xgboost (basically creating a reversible conversion between string and integer as long as you don't change the integer) and train on your current, now hashed as an integer, column.
if the strings are classes, manually naming class numbers (0,1,2,...,n) in a new column and train on that one.
There are definitely other, better ways. As for the second part of your question, left a comment.
Now, your XGBoost model and your dask-cudf dataframe per-GPU allocation must fit on a single GPU, or you will get memory errors. If your model will be considering a large amount of data, please train on the largest GPU memory sized cluster you can. A100s can have 40GB and 80GB. Some older compute GPUs, V100 and GV100 have 32GB. A6000 and RTX8000 have 48GB. then it goes to 24, 16, and lower from there. Please size your GPUs accordingly
I think the same purpose among sklearn.OneHotEncoder, pandas.get_dummies, and keras.to_categorical. But I don't know the difference.
Apart from the difference of the output/input type there is no difference, they all achieve the same result.
There's some technical difference:
Keras is very simple, you give him the target vector and he one -hot encodes it, use keras if you need to encode the labels vector.
Pandas is the most complex, it creates a new column for every class of the data, the good part is that works on dataframes where you want to one-hot only one of the columns (so you could say this is more of a multi purpose method, but not the preferable option if you need to train a NN)
Sklearn lets you one-hot encode multiple features in the same variable, is a bit more flexible that the use keras offers, if the method from keras is too simple try with sklearn, if keras is enough stick with it.
I am running the Word2Vec implementation from gensim twice, and I have a problem with the save function:
model_ = gensim.models.Word2Vec(all_doc, size=int(config['MODEL']['embed_size']),
window=int(config['MODEL']['window']),
workers=multiprocessing.cpu_count(),
sg=1, iter=int(config['MODEL']['iteration']),
negative=int(config['MODEL']['negative']),
min_count=int(config['MODEL']['min_count']), seed=int(config['MODEL']['seed']))
model_.save(config['BASIC']['embedding_dir'])
I obtain different outputs for each time I run it. The first time it gives an "output_embedding", an "output_embedding.trainables.syn1neg.npy" and an "output_embedding.wv.vectors.npy". But the second time it does not give the two npy files, it just generates "output_embedding".
The only thing I change from the first to the second time is the sentences I use as input (all_doc).
Why it does not generate the 3 files ?
Gensim only creates the separate files when the size of the internal numpy arrays is over a certain threshold – so I suspect your all_doc corpus has a very small vocabulary in one case, and a more typically large vocabulary in the other.
When it does generate multiple files, be sure to keep them all together for later loads to work.
(If for some urgent reason you needed to change that behavior, the inherited .save() method takes an optional sep_limit argument to change the threshold - but I'd recommend against mucking with this.)
Separately: that your file names have .trainables. in them suggests you're using a pre-4.0.0 version of Gensim. There've been some improvements to Word2Vec & related algorithms in the latest Gensim, and some older code will need small changes to keep working, so you may want to upgrade to the latest version before building any more functionality on an older base.
I'm working on Tensorflow with GPU. I was curious about data format of tensor. I thought that data are stored in GPU/CPU in Row Major.
However, if I want to store the data in column-major in one operation(Op), can I change the data format only for that operation(Op)? (ex. put some options in function indicates change the format of data)
For instance in matmul operation, there exist options related to transpose. Is there any change in data format (Column Major / Row Major) if I transpose the matrix?
Thanks.
Yes, the default data format is row-major which is opposite to Eigen.
If you are using Python, then you will need to transpose your data when simulating a col-major layout. When using C++ nothing prevents you from employing Eigen::RowMajor instead.
The matmul has options transpose_a and transpose_b as (cu-)BLAS can handle both formats without explicit transpose, e.g. see GEMM. So it will not change your data format. It is only a trick, to prevent additionals launch of CUDA kernels or other functions beforehand to minimize run-time.
It is part of the BLAS specification, e.g. see LAPACK
I've using tensorflow to build word2vec model,reference here：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py#L118
my question is that, how can i find top n similar words for a certain word.I know in gensim, I can save and load word2vec model,and then use model.most_similar to find what I want.but how in tensorflow and even more is there any way to save model in tensorflow since i find what i get is only an embedding vector,is that right?
I think as long as you have computed the weight vector for each token, then you can manipulate all the tokens in the vector space. You can simply calculate the cosine similarity between each vector and then sort by score. For your reference, you can look at the source code of most_similar method implemented in gensim word2vec model. Hope this helps.