h2o

问题 I've been working with h2o and h2o Flow for the past days and have loved it. Two days ago I exported some models (in binary format) from h2o Flow and imported them with R so I could do further studies. It worked perfectly until today. For some reason, I'm getting the following error when I try the h2o.loadModel function (which worked just fine before with all my exported binary models). Just to clarify, I already ran successfully the h2o.init() command to open the H2O cluster. ERROR:

问题 I have already read this question: How should we interpret the results of the H2O predict function? Still don't understand if p1 is the probability between [0,1] and could be used equally as it 's a regression and i can apply my own threshold edit: thank you for your answer still have some confusion about it, let's dig it suppose my outcome Y is [0,1], if Y is numeric i run it as REGRESSION and i have a single column as response. On the other hand if Y is factor run it as CLASSIFICATION and

问题 I need to install an older version of H2O because model loading doesn't work even if the versions are just one apart (3.26.0.2 vs. 3.26.0.3). I'm struggling to find a page from which I can find the download links. Why doesn't it exist? All software have an archive or older versions page for this. I also tried playing with the link to current version but no luck as it doesn't have a pattern you could guess. So how can I install H2O 3.26.0.2 in Python (pip)? Looked all over the web and

问题 I need to install an older version of H2O because model loading doesn't work even if the versions are just one apart (3.26.0.2 vs. 3.26.0.3). I'm struggling to find a page from which I can find the download links. Why doesn't it exist? All software have an archive or older versions page for this. I also tried playing with the link to current version but no luck as it doesn't have a pattern you could guess. So how can I install H2O 3.26.0.2 in Python (pip)? Looked all over the web and

问题 Running h2o (http://h2o-release.s3.amazonaws.com/h2o/rel-yau/5/h2o-3.26.0.5-hdp3.1.zip) on hdp 3.1.4 getting error at startup due to access restrictions to the hdfs:///user/hdfs folder [root@HW005 h2o-3.26.0.5-hdp3.1]# hadoop jar h2odriver.jar -nodes 4 -mapperXmx 6g Determining driver host interface for mapper->driver callback... [Possible callback IP address: 172.18.4.83] [Possible callback IP address: 127.0.0.1] Using mapper->driver callback IP address and port: 172.18.4.83:37342 (You can

问题 At first I thought it was a random issue, but re-running the script it happens again. Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = urlSuffix, : Unexpected CURL error: Recv failure: Connection reset by peer I'm doing a grid search on a medium-size dataset (roughly 40000 x 30) with a Gradient Boosting Machine model. The largest tree in the grid is 1000. This usually happens after running for a couple of hours. I set max_mem_size to 30Gb. for ( k in 1:nrow(par.grid

问题 I run my model with H2o library. I run with 5 folds cross-validation. model = H2OGradientBoostingEstimator( balance_classes=True, nfolds=5, keep_cross_validation_fold_assignment=True, seed=1234) model.train(x=predictors,y=response,training_frame=data) print('rmse: ',model.rmse(xval=True)) print('R2: ',model.r2(xval=True)) data_nfolds = model.cross_validation_fold_assignment() I got the cross-validation fold assignment. I try to reuse it for a new model with other parameters such as ntrees or

问题 I am trying to understand how deep features are made in an autoencoder. I created an autoencoder with h2o.deeplearning and then I tried to calculate the deepfeatures manually. The autoencoder fit = h2o.deeplearning( x = names(x_train), training_frame = x_train, activation = "Tanh", autoencoder = TRUE, hidden = c(25,10), epochs = 100, export_weights_and_biases = TRUE, ) I used as activation function Tanh and 2 hidden layers with no dropout, to make the things simple. Calculating hidden layer 1

问题 My code: curl -X POST http://localhost:54321/3/ParseSetup --data 'source_frames=["/root/documents/my_file.csv"]' Error: java.lang.IllegalArgumentException: Key not loaded: Key at water.api.ParseSetupHandler.guessSetup(ParseSetupHandler.java:31) Help: Could anyone help to resolve this? Am I missing any parameter? Used H2O version: h2o-3.10.0.10 回答1: My suggestion is to open Flow (http://localhost:54321) in a browser, then start Firebug (or the equivalent in your browser of choice), and the

问题 Similar to this question in R here, I get out of memory issues when running loops with grid search in H2O. In R, doing gc() during each loop did help. What is the proposed solution here? 回答1: There appears to be no h2o.gc() function in the Python API. See "How can I debug memory issues?" in the FAQ. You could POST that back-end command ( GarbageCollect ) directly using the REST API if you suspect the problem is the back-end holding on to memory that it no longer should be. Studying the