h2o-3 | Open Source , Distributed , Fast & Scalable Machine | Machine Learning library

 by   h2oai Jupyter Notebook Version: jenkins-master-6230 License: Apache-2.0

kandi X-RAY | h2o-3 Summary

kandi X-RAY | h2o-3 Summary

h2o-3 is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Spark applications. h2o-3 has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Flow notebook/web interface, and works seamlessly with big data technologies like Hadoop and Spark. H2O provides implementations of many popular algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks, Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML). H2O is extensible so that developers can add data transformations and custom algorithms of their choice and access them through all of those clients. H2O models can be downloaded and loaded into H2O memory for scoring, or exported into POJO or MOJO format for extemely fast scoring in production. More information can be found in the H2O User Guide. H2O-3 (this repository) is the third incarnation of H2O, and the successor to H2O-2.

            kandi-support Support

              h2o-3 has a medium active ecosystem.
              It has 6315 star(s) with 1969 fork(s). There are 391 watchers for this library.
              It had no major release in the last 6 months.
              There are 2633 open issues and 6323 have been closed. On average issues are closed in 21 days. There are 66 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of h2o-3 is jenkins-master-6230

            kandi-Quality Quality

              h2o-3 has no bugs reported.

            kandi-Security Security

              h2o-3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              h2o-3 is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              h2o-3 releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

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            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of h2o-3
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            h2o-3 Key Features

            No Key Features are available at this moment for h2o-3.

            h2o-3 Examples and Code Snippets

            How to get all base model from a stackedsamble in h2o
            Pythondot img1Lines of Code : 6dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            {base_model: h2o.get_model(base_model).actual_params
             for base_model in h2o.get_model("model_id").base_models}
            metalearner_params = h2o.get_model("SE_model_id").metalearner().actual_params
            se_params = h2o.get_model
            copy iconCopy
            def moleculeMass(moleculeName):
                total = 0
                for i in moleculeName:
                    if i.isalpha():
                        mass_p = mass[i]
                        total += mass_p
                        total += mass_p * (int(i) -1)
                return total
            Run h2o.ai as a service (background)
            Pythondot img3Lines of Code : 5dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            java -jar h2o.jar
            import time
            Convert *.csv file in DB with repeat second header several times
            Pythondot img4Lines of Code : 36dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import sqlite3
            import io
            def load_csvfile(filepath):
                with open(filepath) as csvfile:
                    data = io.StringIO()
                    line = csvfile.readline()
                    line = f"POWERON;{line.rsplit(';', 1)[0]}\n"
            Python how to split a string into several phases
            Pythondot img5Lines of Code : 42dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import re
            str1 = 'Polyphosphate + n H2O <=> (n+1) Oligophosphate'
            str2 = '16 ATP + 16 H2O + 8 Reduced ferredoxin <=> 8 e- + 16 Orthophosphate + 16 ADP + 8 Oxidized ferredoxin'
            res1 = [i.lstrip(" 123456789n()+").strip() for i 
            GLRM in H2O - Performance Metrics return NaN
            Pythondot img6Lines of Code : 21dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import h2o
            from h2o.estimators import H2OGeneralizedLowRankEstimator
            # Import the USArrests dataset into H2O:
            arrestsH2O = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
            # Spl
            Saving `h2o_model.accuracy` printed output to a file
            Pythondot img7Lines of Code : 7dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import os
            import h2o
            !H2O_MODEL={h2o_model.key} jupyter nbconvert --to html --execute template.ipynb --output={h2o_model.key}_results.html
            How to set weights_column in H2OAutoML?
            Pythondot img8Lines of Code : 2dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            aml.train(x=x, y=y, training_frame=train, weights_column='weight')
            Get accuracy metric from leaderboard function when using H20 AI
            Pythondot img9Lines of Code : 2dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            aml = H2OAutoML(max_runtime_secs = 30, sort_metric = "logloss")
            h2o: F1 score and other binary classification metrics missing
            Pythondot img10Lines of Code : 6dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            failure    2
            dtype: int64
            train["response"] = train["response"].asfactor()

            Community Discussions


            Using RNN Trained Model without pytorch installed
            Asked 2022-Feb-28 at 20:17

            I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

            I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

            I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.



            Answered 2022-Feb-17 at 10:47

            You should try to export the model using torch.onnx. The page gives you an example that you can start with.

            An alternative is to use TorchScript, but that requires torch libraries.

            Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

            ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

            A running example

            Just modifying a little your example to go over the errors I found

            Notice that via tracing any if/elif/else, for, while will be unrolled

            Source https://stackoverflow.com/questions/71146140


            Flux.jl : Customizing optimizer
            Asked 2022-Jan-25 at 07:58

            I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.


            I'm using MNIST dataset.



            Answered 2022-Jan-14 at 23:47

            Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.

            Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:

            Source https://stackoverflow.com/questions/70641453


            How can I check a confusion_matrix after fine-tuning with custom datasets?
            Asked 2021-Nov-24 at 13:26

            This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.


            I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.

            Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.

            After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?

            An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image



            Answered 2021-Nov-24 at 13:26

            What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.

            Source https://stackoverflow.com/questions/68691450


            CUDA OOM - But the numbers don't add upp?
            Asked 2021-Nov-23 at 06:13

            I am trying to train a model using PyTorch. When beginning model training I get the following error message:

            RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)

            I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi I see these processes running



            Answered 2021-Nov-23 at 06:13

            This is more of a comment, but worth pointing out.

            The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):

            Let's run the following python commands interactively:

            Source https://stackoverflow.com/questions/70074789


            How to compare baseline and GridSearchCV results fair?
            Asked 2021-Nov-04 at 21:17

            I am a bit confusing with comparing best GridSearchCV model and baseline.
            For example, we have classification problem.
            As a baseline, we'll fit a model with default settings (let it be logistic regression):



            Answered 2021-Nov-04 at 21:17

            No, they aren't comparable.

            Your baseline model used X_train to fit the model. Then you're using the fitted model to score the X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.

            The grid searched model is at a disadvantage because:

            1. It's working with less data since you have split the X_train sample.
            2. Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of X_val per fold).

            So your score for the grid search is going to be worse than your baseline.

            Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.

            So how should one go about conducting a fair comparison?

            1. Split your training data for both models.

            Source https://stackoverflow.com/questions/69844028


            Getting Error 524 while running jupyter lab in google cloud platform
            Asked 2021-Oct-15 at 02:14

            I am not able to access jupyter lab created on google cloud

            I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.

            I tried the diagnostic tool, which gave the following result:

            but didn't fix it.

            Thanks in advance.



            Answered 2021-Aug-20 at 14:00


            TypeError: brain.NeuralNetwork is not a constructor
            Asked 2021-Sep-29 at 22:47

            I am new to Machine Learning.

            Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:

            I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!

            Kindly point out what I am missing here!

            Find below my code:



            Answered 2021-Sep-29 at 22:47

            Turns out its just documented incorrectly.

            In reality the export from brain.js is this:

            Source https://stackoverflow.com/questions/69348213


            Ordinal Encoding or One-Hot-Encoding
            Asked 2021-Sep-04 at 06:43

            IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?

            I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:



            Answered 2021-Sep-04 at 06:43

            You're right. Just one thing to consider for choosing OrdinalEncoder or OneHotEncoder is that does the order of data matter?

            Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:

            • quality = ["bad", "average", "good", "excellent"] or
            • shirt_size = ["large", "medium", "small"]

            but it is obviously not the case for the:

            • color = ["white","orange","black","green"]

            column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white category should be encoded as 0 and black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)

            Source https://stackoverflow.com/questions/69052776


            How to increase dimension-vector size of BERT sentence-transformers embedding
            Asked 2021-Aug-15 at 13:35

            I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language

            by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.




            Answered 2021-Aug-10 at 07:39

            Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.

            Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.

            If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.

            Source https://stackoverflow.com/questions/68686272


            How to identify what features affect predictions result?
            Asked 2021-Aug-11 at 15:55

            I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.

            Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?

            Table example:



            Answered 2021-Aug-11 at 15:55

            You could build a model like this.

            x = features you have. y = true_lable

            from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).

            Source https://stackoverflow.com/questions/68744565

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install h2o-3

            From the command line, validate python is using the newly installed package by using which python (or sudo which python). Update the Environment variable with the WinPython path.
            If you don't have Homebrew, we recommend installing it. It makes package management for OS X easy.
            Install Java 8. Installation instructions can be found here JDK installation. To make sure the command prompt is detecting the correct Java version, run:. Installation instructions can be found here R installation. Click “Download R for Linux”. Click “ubuntu”. Follow the given instructions. To install the required packages, follow the same instructions as for OS X above. Note: If the process fails to install RStudio Server on Linux, run one of the following: sudo apt-get install libcurl4-openssl-dev or sudo apt-get install libcurl4-gnutls-dev.
            Documentation for each bleeding edge nightly build is available on the nightly build page.


            In the h2o-hadoop directory, each Hadoop version has a build directory for the driver and an assembly directory for the fatjar.
            Find more information at:

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