method, but its usage is meant strictly for the scikit-learn Pipeline and grid search model object supports all of the metric accessor methods as a model. When you first load Flow you will see the Flow menu, as shown in Figure 1-4. There are many tools for directly interacting with user-visible objects in the H2O cloud. After that we will dive into our first machine learning project: load some data, make a model, make some predictions, and evaluate success. H2O uses a distributed key-value store (the “DKV”) that contains pointers to the Choose the “test” data frame, and click the Predict button to set it going. H2O works equally well with Python 2.7 or Python 3.5, as should all the examples in this book. As you can see in Figure 1-9, you get a graphical output; other options allow you to see the parameters the model was built with, or how training progressed. So let’s take a look Of course, it was good luck for the person needing a paper accepted by a journal! In the previous snippet, a has not yet triggered any big data evaluation and is, in package. I installed it, tried again, and this time everything was fine. Data in H2O is compressed and is You can find This means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.. Easy-peasy, lemon-squeezy. out of sight in a singleton instance of H2OConnection (this is the global can be performed with the mental model of operating on 2D frames (i.e. In Python they are like a DataFrame in pandas (or a dict of equal-length list). all dependent subparts composing a are also evaluated. To run one of the Java or Scala sample programs, use bin/run-example [params] in the top-level Spark directory. If you really want to install it this way, follow the instructions on starting H2O from the command line in Chapter 10. It worked. (Behind the scenes, this invokes the more general spark-submit script for launching applications). ), As with the data frames, m is a class wrapper around a handle, pointing to the actual model stored on the H2O cluster. Even the beginners in python find it that way. everything is an Exercise your consumer rights by contacting us at donotsell@oreilly.com. the available metrics for various model categories, please refer to the “Metrics in H2O” section I tried 25 different random splits, which ended up ranging from 111/39 to 130/20, and made 20 models on each. The other three columns show its confidence. a wider audience of data and machine learning devotees who work exclusively with Python. 5 It is the ability to make inferences like this that separate the common herd from the data scientist. multiple clients to attach to a single H2O cloud). On the Select Components page it wants to install both the 32-bit and 64-bit versions; I chose to only install 64-bit, but there is no harm in installing both. programming model for interacting with H2O from Python. It is actually just another client, written in CoffeeScript (a JavaScript-like language) this time, making the same web service calls to the H2O backend that the R or Python clients are making. You can see it is really sure that it was a setosa. Then this new two-column data frame is downloaded. That command takes care of any dependencies, too. If this is your first time using CRAN4 it will ask for a mirror to use. In your own code I recommend meaningful names, e.g., premierLeagueScores2005_2015_train instead of train. ), View predictions you have generated through your client. # score and compute new metrics on the test data! I’m going to spend a few pages going through this in detail, but I want to just emphasize that this is the complete script: it downloads data, prepares it, creates a multi-layer neural net model (i.e., deep learning) that is competitive with the state of the art on this data set, and runs predictions on it. the model was trained upon. You are thinking “Aha! You may wonder if the choice of R or Python matters? If not, the error message should be telling you what dependency is missing, or what the problem is. purpose is to cut down on the chatter between the client (a.k.a the python interface) and If you don’t have pandas installed, you get a nested list, something like this: You could do analysis with that. 14 This is more common with small data sets like Iris; with larger data sets it is less likely to happen unless, for instance, one category in an enum column is much rarer than other categories. Hopefully you got suspicious as soon as I said, “This is great!” We’re choosing our cutoff criteria of 0.97 based on looking at our test results, after being told the correct answers. data is “in memory”), and not in the python process local '/usr/local/Cellar/python/2.7.9/Frameworks/Python.framework/Versions/2.7/local/h2o_jar/h2o.jar', '/Users/spencer/Library/Python/2.7/h2o_jar/h2o.jar'], -------------------------- ---------------------------, H2O cluster uptime: 13 seconds 903 milliseconds, 0 GBM_model_python_1447790800404_2. The final line of the listing, , was p <- h2o.predict(m, test) and it makes predictions using the model m. Again, p is a handle to a frame on the H2O server. many things, but the primary purpose of H2O is as a distributed (many machines), H2O AutoML can be used to automate a large portion of the machine learning workflow, which includes automatic training and tuning of many models within … How did that affect things? Learning Objectives • Start and connect to a local H2O cluster from Python. In the second approach, one column from p is downloaded to R, then one column from test is downloaded, and then they are combined in R’s memory, to make a two-column data frame. (If you are not using R, you might want to jump ahead to “Install H2O with Python (pip)”.). By typing as.data.frame(p) I see all 30 predictions (just a few shown here): The predict column in the first row is the class it is predicting for the first row in the test data. It seems the randomness in our split perhaps matters more than the randomness in our model,14 because one split gave a perfect score for all of its 20 models (it had 129 rows to train from, 21 to test on), whereas another only averaged 90% (it had 114 to train from, 36 to test on). the objects available in this module it is helpful to understand how these objects map to You should see there are buttons to split the data (into training/test frames), or build a model, and also that it lists each column. Alternatively, choose one in a place you’d like to visit, put your shades on, and take a selfie. Data collection is easy. • Perform basic data transformation and exploration. In this first chapter we have covered a lot of ground: Importing data, making models, and making predictions…, …in any of R, Python, or the browser-based Flow UI. Also think about how it might be a billion rows, too many to fit in our client’s memory. H2O AI Hybrid Cloud I will not be showing Flow examples in the rest of the book, though the knowledge learned as we look at the algorithm parameters in later chapters can be directly applied to models built in Flow. is no direct manipulation of the JVM. H2O is a Java … If you choose “getFrames” from the main menu, either after doing the preceding steps or after loading the data from R or Python, you would see an entry saying “iris_wheader.hex” and that it has 150 rows and 5 columns. Am I missing any step? R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), … If you haven’t heard of the Iris data set before, this must be your first machine learning book! Take O’Reilly online learning with you and learn anywhere, anytime on your phone and tablet. submodule. (Remember this was on the data it had seen.). Get Practical Machine Learning with H2O now with O’Reilly online learning. mlflow.pyfunc. data and provide a host of metrics on the validation and training data. To list the current contents of the H2O cluster, you can use the h2o.ls command: There are models, data, and model metrics all floating around in the DKV. H2O is running Java 8 If you do not wish to use Python, H2O-3 has a GUI API, H2O Flow, which can be accessed on a browser; the python client was easy to use and flexible, with intuitive commands and other python benefits such as numpy, pandas, and opencv. (If not, pip install pandas should be all you need to install it.) However, their primary In the guts of this module is the ExprNode class, which defines objects holding H2OFrame: Lazy expressions will be discussed briefly in the coming sections, as they are not Model building in this python module is influenced by both scikit-learn and the H2O R No, and why will be explained shortly. For support of other GUI frameworks, LaTeX rendering, saving animations and a larger selection of file formats, you need to install additional dependencies. And you will need Java. 10 The APIs also call this x, though if you ever poke your nose into the REST API, you will see it actually receives the complement of x: the list of field names to not use, called ignored_columns. Let’s quickly recap what we have. As long as you have installed pandas, p.as_data_frame() will instead give: What else can we do? conditionals), broadcasting operations, and a slew of math operations for transforming and attributes. In the last example, a validation data set was also provided about parallelism and distribution of work. H2O from Python is a tool for rapidly turning over models, doing data munging, and To test it, start Python, type import h2o, and if that does not complain, follow it with h2o.init(). As we will see in “On Being Unlucky”, you almost certainly got 0.900 (3 wrong), 0.933 (2 wrong), 0.967 (1 wrong), or 1.000 (perfect score). 1 modelmetrics_GBM_model_python_1447790800404_2@... 2 prostate.hex, 3 py_2, [5.843333333333335, 3.0540000000000007, 3.7586666666666693, 1.1986666666666672], # attempting to use fr results in an attribute error, # , # pass a validation frame in addition to the training frame, # display the model summary by default (can also call m.show()), # show the performance on the training data, (can also be m.performance(train=True), # show the performance on the validation data. To install tpot and its core dependencies you can use: conda install -c conda-forge tpot To install additional dependencies you can use: H2O from Python is a tool for rapidly turning over models, doing data munging, and building applications in a fast, scalable environment without any of the mental anguish about parallelism and distribution of work. In R that can be done with mean(p$predict == test$class), which tells me 0.933. The model works pretty well using a similar Keras code above, though I want to implement the same using H20.ai This documentation here on H2O Deep Water says "The H2O Deep Water project supports CNNs and RNNs though third-party integrations of other deep learning libraries such as TensorFlow, Caffe and MXNet. I suspect this kind of bad luck15 was involved in a few of them. (splitting into training and test data) is another big concept, which boils down to trying not to overfit. As you can see, the new model metrics object generated by calling model_performance on the This is the main flavor that can be loaded back into H2O. Import the h2o Python module and H2OAutoML class and initialize a local H2O cluster. The conceptual paradigm MapReduce (AKA “divide and conquer H2O is an open source Machine Learning framework with full-tested implementations of several widely-accepted ML algorithms. Just like with the data, the model is stored on the H2O cluster, and m is just a handle to it. There is a single active connection to the H2O JVM at any time, and this handle is stashed and categorical data automatically differently, depending on the algorithm. There is another way we could have found out what percentage it got right. Here’s an example showing how a list comprehension is That’s it. On a final note, each of H2O’s algorithms handles missing (colloquially: “missing” or “NA”) The Flow commands you see can be saved as scripts, and loaded back in later. Now click the “View” button (a magnifying glass icon). From the command line, type pip install -U h2o. The H2O library can simply be installed by running pip. If you load data in Flow, you can see it in R/Python. H2O is extensible and users can build blocks using simple math legos in the core. during model training, so there is an extra set of metrics on this validation set that is the cumulative, unevaluated expressions that underpin H2OFrame objects. It has also realized (from analyzing the data) that the “class” column was categorical, which means we will be doing a multinomial categorization, not a regression (see “Jargon and Conventions”). For example, it’s possible to assign all Scala, Java, Python and R examples are in the examples/src/main directory. ), Click Split (the scissors icon), then change the 0.25 to 0.2. If I output p I only see the first six predictions. In this book, I will put the name of this column in a variable called y.9 (In unsupervised learning y will not be set. that may or may not be lazy. This includes all types of slicing (with complex Next, import the libraries in your jupyter notebook. It is one of the examples of how we are using python for stock market and how it can be used to handle stock market-related adventures. classification task rather than a regression task. The rest of this document explains a few of the client-server details and the general Of course, our “cluster” is on localhost, on the same machine as our client, so it is all the same system memory. After a few seconds glaring at the screen, I shrugged, and downloaded the latest version of the JDK. In this age of big data, companies across the globe use Python … By default, your H2O instance will be allowed to use all your cores, and (typically) 25% of your system memory. on other clients (multi-tenancy is not a supported model of use, but it is possible for H2O is an open source distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). (If you are not interested in using Python, skip ahead to “Our First Learning”.). In addition, every metric that H2O displays in the Flow is calculated on the backend and stored for each model. The platform includes interfaces for R, Python, Scala, Java, JSON and CoffeeScript/JavaScript, along with a built-in web interface, Flow, Even better, you can start a long-running model from Python or R, then go over to Flow and get immediate feedback on how the training is going. 7 You perhaps see some deprecation warnings? Katamari’d together and only by an IP address and a port) via a series of REST calls (see connection.py for the REST methods to the following five model categories: Let’s build a logistic regression using H2O’s GLM: As you can see the model setup and train is akin to the scikit-learn style. To see the possible First start the Python prompt by typing the following command − $ Python3 Once the Python interpreter starts, type the following Python statement on the Python command prompt − >>>import h2o The next question you are likely to have is which ones, if any, did H2O’s model get wrong? Here’s an example There are two approaches so, based on what you know so far, have a think about the difference between this: In the first approach, p$predict and test$class are combined in the cluster to make a new data frame in the cluster. 1 Chapter 10 shows some alternative ways to install H2O (see “Installing the Latest Version”) including how to compile it from source. from the command line that you need to run H2O, but RStudio makes everything easier to use (especially on Windows, where the command line is still stuck in 1995). (so if you’re not in The Valley, no sweat, we’re probably coming to your area soon! On a Mac, see Using Python on a Macintosh. MetricsBase. being constructed will change over time); and actions by the client may have side-effects It is fully featured, by which I mean that you can do all of the following: View data you have uploaded through your client, View models you have created through your client (and those currently being created! Here is a brief example of H2O on Hadoop: ['/usr/local/Cellar/python/2.7.9/Frameworks/Python.framework/Versions/2.7/h2o_jar/h2o.jar'. But!! The goal of H2O is to allow simple horizontal scaling to a given problem in order to Start H2O. H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. and our JIRA ticketing system (for complete code refer GitHub) Stocker is designed to be very easy to handle. This is great, as it means we can mark results with confidence below 0.97 as suspicious. I write about learning to program, and the best ways to go about it on amymhaddad.com. 6 See http://bit.ly/2gn5h6e for how to query your Java installation and get the default for your system. Additionally, H2O can perform on-the-fly scoring of validation fact, a pending computation. __H2OConn__). held in the JVM heap (i.e. It has a vast field of usages like the development of desktop graphical user interface (GUI), apps development, development of websites, development of scientific applications and numeric applications, development of games, etc. Can be parsed from various timestamp formats. A set of categories or classes. And how good it is. From the start screen click the “importFiles” link, or from the menu at the top of the screen choose Data then Import Files. H2O provides interfaces for Python, R, Java and Scala, and can be run in standalone mode or on a Hadoop/Spark cluster via Sparkling Water or sparklyr. Chapter 2 will delve into this topic further, but how about (p["predict"] == test["class"]).mean() to tell us the percentage of correct answers? Trust me. The focus of this talk is scalable machine learning using the H2O R and Python packages. For the screenshot I ignored them with import warnings;warnings.filterwarnings("ignore", category=DeprecationWarning), but normally I ignore them by turning a blind eye. When I try to install H2O in Windows where I run Python, I get the below message. H2OEstimator implements a fit The final line of the listing, , was p = m.predict(test), and it makes predictions using this model, and puts them in p. Here are a few of the predictions. Terms of service • Privacy policy • Editorial independence. The version of H2O on CRAN might be up to a month or two behind the latest and greatest. illustrates the first major concept we need to understand when using H2O: all the data is on the cluster (the server), not on our client. In a medical application that could mean doing another test to get a second opinion; in a financial trading application (or a poker app) it could mean sit this one out, too risky. mutating a Frame – all the while the actual Big Data is sitting in the H2O cloud. After a detour to look at how random elements can lead us astray, the chapter will close with a look at the web interface, Flow, that comes with H2O. google group mailing list, of this document. validation, and finally testing. Below is the msg. (Python is a dependency of pip, so by installing pip we get Python too.) Last, but not least, we regularly engage the machine learning I’ve kept the names short: this is a book, and word-wrap in listings is ugly; some people might even be reading it on their phone. (rather than data objects as they would be in scikit). And, I don't see the H2O package in the Libraries list in the Python IDE. In Python: data.split_frame([0.8],seed=99). scikit-learn software with H2O-powered algorithms. You need Java installed, which you can get at the Java download page. H20 Deep Learning for Classification Early Access Released on a raw and rapid basis, Early Access books and videos are released chapter-by-chapter so you get new content as it’s created. For application developers and data scientists, the gritty details of thread-safety, Finally to install TPOT itself, run the following command: pip install tpot conda-forge. We also glossed over quite a few options, and the next chapter (well, in fact, the whole rest of the book) will start digging in deeper. print-to-screen). On Windows go to http://cran.rstudio.com/bin/windows/ and download and run the Just about all you can do with them, within H2O, is convert them to enum; they cannot be directly used to build models from. But you should be thinking as if they are on opposite sides of the globe. provided to the model post-build via the model_performance call will produce a set I think that deserves another selfie: in fact, make it a video of you having a little boogey-woogey dance at your desk, then post it on social media, and mention you are reading this book. The correct species is in test$class, while deep learning’s guess is in p$predict. If we study the confidence of our predictions we see the correct answers are mostly over 0.99, with the least-confident correct answer being 0.97. In supervised machine learning one of these columns will be what we want to predict. Briefly, what we are doing is (randomly) choosing 80% of our data to train on, and then we will try using our model on the remaining 20%, to see how well it did. Every new python session begins by initializing a connection between the python client and As a bonus, it can be installed as a normal user, which is helpful for when you do not have root access. You just have to pick up the algorithm from its huge repository and apply it to your dataset. community all over the nation with a very busy meetup schedule counterparts in the H2O cloud using a remove call: Notice that attempting to use the object after a remove call has been issued will Choose the JDK.3 If you think you have the Java JDK already, but are not sure, you could just go ahead and install H2O, and come back and (re-)install Java if you are told there is a problem. You will be happy to know that H2O is very easy to install. and finally, we host our very own H2O World conference. produced as a result of the training (and stored in the model). Don’t worry about what your coworkers think. Let’s check that it worked by typing library(h2o). You might want to do this if you hit a bug that has only been fixed in the latest development version, or if you want to start hacking on H2O itself. ! If you want to delete something from the DKV, you can do this with the h2o.remove On Linux, sudo apt-get python-pip on Debian/Ubuntu/Mint/etc. What about our incorrect answers? ), Some or all of the other columns in your data are what we learn from. Use h2o.shutdown() to, well, see if you can guess what it does. It is a set of 150 observations of iris plants, with four measurements (length and width of each of sepal and petal) and the species it belongs to. Linux, Mac, and Windows versions are available. Remember to choose a 64-bit install (unless you are stuck with a 32-bit version of Windows, of course). H2OConnection: If no jar is found, then an EnvironmentError will be raised: After making a successful connection, you can obtain a high-level summary of the cluster As shown in Figure 1-3, the client just has handles (pointers) to the actual data on the H2O cluster. There are three species represented, with 50 observations each. But wait, there’s more! If you clicked the “iris_wheader.hex” link you would see Figure 1-7. Thanks in advance! objects in the JVM. Above that, the confusion matrix tells us that it incorrectly guessed two virginica samples as being versicolor. To find out, I tried remaking the model 100 times (using random seeds 1 to 100). You can see it is over 99.5% certain about all its answers here: Just as before, this is a frame on the H2O cluster, so when you see it, you only you see a preview, the first 10 rows.