Last Updated on February 4, In this post you will discover how to save and load your machine learning model in Python using scikit-learn.

Discover how to prepare data with pandas, fit and evaluate models with scikit-learn, and more in my new bookwith 16 step-by-step tutorials, 3 projects, and full python code.

You can use the pickle operation to serialize your machine learning algorithms and save the serialized format to a file. The example below demonstrates how you can train a logistic regression model on the Pima Indians onset of diabetes dataset, save the model to file and load it to make predictions on the unseen test set update: download from here.

Load the saved model and evaluating it provides an estimate of accuracy of the model on unseen data. It provides utilities for saving and loading Python objects that make use of NumPy data structures, efficiently. This can be useful for some machine learning algorithms that require a lot of parameters or store the entire dataset like K-Nearest Neighbors.

### Using Python & Stan to pick the Superbowl Winner with an Unrealistic Model

The example below demonstrates how you can train a logistic regression model on the Pima Indians onset of diabetes dataset, saves the model to file using joblib and load it to make predictions on the unseen test set. After the model is loaded an estimate of the accuracy of the model on unseen data is reported. Take note of the version so that you can re-create the environment if for some reason you cannot reload your model on another machine or another platform at a later time. In this post you discovered how to persist your machine learning algorithms in Python with scikit-learn.

Do you have any questions about saving and loading your machine learning algorithms or about this post? Ask your questions in the comments and I will do my best to answer them. Covers self-study tutorials and end-to-end projects like: Loading datavisualizationmodelingtuningand much more Hey, i trained the model for digit recognition but when i try to save the model i get the following error. Please help. Can we save it as a python file. I have two of your books and they are awesome.

I took several machine learning courses before, however as you mentioned they are more geared towards theory than practicing. I devoured your Machine Learnign with Python book and 20x my skills compared to the courses I took. As Jason already said, this is a copy paste problem. In your line specifically, the quotes are the problem. If you could help me out with the books it would be great.

Real applications is not single flow I found work around and get Y from clf. What is correct solution? Should we pickle decorator class with X and Y or use pickled classifier to pull Ys values? I would not suggest saving the data.Stan, developed by a team led by Andrew Gelman, is one of the leading languages to do probabilistic computing.

The core of probabilistic computing lies in Bayesian statistics. Stan gets its name in honor of Stanislaw Ulam, co-inventor of the Monte Carlo method, the computational engine behind all Bayesian computing. And PyStan is the Python interface to Stan. In most of statistics, we start with observed data and try to infer the process that generated data. One approach is to assume the data was generated by a probabilistic model with one or more parameters.

And one is typically interested in estimating the parameters from the observed data. Bayesian approach uses the observed data as constant and the unknown parameters to have their own probability distribution.

Without getting into the details of statistics or computing behind using PyStan, in this post we will illustrate a simple example of using PyStan. We will see a classic statistics problem, where we have observed data and want to estimate the parameters of the model that generated the data.

By starting with a parametric model, we can estimate parameters of a simple model in many ways. For example, we can use Maximum Likelihood Estimate and get a point estimate for the parameters defining the model.

The advantage of using Stan, a Bayesian approach is that we get the full distribution of the parameter, not just most likely value of the parameter. Let us make sure, we have Pystan installed and it works nicely. The pystan version used in this example is 2. Let us simulate our observed data using normal probability distribution. Let us also set some random seed to reproduce our results.

Now that we have our data to use and we are ready to estimate the parameters of distribution the data was generated from. The key step of using Stan is to write out the data generating probabilistic model in Stan programming language. In PyStan, we will wrap the Stan model code as a string. All Stan model code must have at least three code blocks.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm trying to implement a hierarchical mixture model in Stan that describes how performance on a task changes over time.

To implement these, I've just assumed that the prior on these lower level parameters is a mixture. For these, I've assumed that the observation itself is drawn from a mixture of normals. I'm pretty sure I've implemented the mixture of the group-level parameters correctly, as I've just done what was in the Stan manual. However, I wanted to check that I've done the hierarchical mixture components of the model correctly. The model is quite inefficient. So I suspect I'm missing something. Learn more.

Asked 2 years ago. Active 1 year, 11 months ago. Viewed times. Tim Tim 71 5 5 bronze badges. I'd recommend Michael's case study on problems with fitting mixture models.

Can you fit the model without the mixture prior? Thanks for the quick reply Bob, and for the referral. Michael's case study was quite helpful. However, when I added the mixture on the priors back in, convergence was quite poor again.

**Bayesian Analysis in Python: A Starter Kit**

Of the four chains, three mix quite nicely. But the forth is getting stuck pretty far out from the others see traceplot here: dropbox. Just a note: I used a thinning interval of 10, so there were iter per chain. Correction to the above: there were iterations per chain. Are you ordering or something to identify the chains? Do their posteriors overlap? To deal with the tails, you can initialize closer to where you want to go or also add even stronger priors.

Hi Bob, sorry for the delay. I am ordering the chains, and there doesn't appear to be any real overlap in the posteriors. I've now gotten the model to converge. Is this an indication that a two-mixture model is likely a poor representation of the data, and that a one-mixture i.

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### Introduction to Probabilistic Programming with PyStan

Sign up or log in Sign up using Google. Sign up using Facebook.The many virtues of Bayesian approaches in data science are seldom understated. Unlike the comparatively dusty frequentist tradition that defined statistics in the 20th century, Bayesian approaches match more closely the inference that human brains perform, by combining data-driven likelihoods with prior beliefs about the world.

This kind of approach has been fruitfully applied in reinforcement learningand efforts to incorporate it into deep learning are a hot area of current research.

Indeed, it has been argued that Bayesian statistics is the more fundamental of the two statistical schools of thought, and should be the preferred picture of statistics when first introducing students to the subject.

As the predictions from Bayesian inference are probability distributions rather than point estimates, this allows for the quantification of uncertainty in the inferences that are made, which is often missing from the predictions made by machine learning methods. Although there are clear motivations for incorporating Bayesian approaches into machine learning, there are computational challenges present in actually implementing them.

Often, it is not practical to analytically compute the required distributions, and stochastic sampling methods such as Markov chain Monte Carlo MCMC are used instead. One way of implementing MCMC methods in a transparent and efficient way is via the probabilistic programming language, Stan. On the right-hand side, we have the likelihood, which is dependent on our model and data, multiplied by the prior, which represents our pre-existing beliefs, and divided by the marginal likelihood which normalises the distribution.

This theorem can be used to arrive at many counterintuitive results, that are nonetheless true. Take, for instance, the example of false positives in drug tests being much higher when the test population is heavily skewed. Bayesian inference is hard.

## STAN for linear mixed models

The reason for this, according to statistician Don Berry:. Well, OK. But more concretely, Bayesian inference is hard because solving integrals is hard. That P B up there involves an integral over all possible values that the model parameters can take. In generating those samples, we need a methodological framework to govern how the sampler should move through the parameter space. A popular choice is Markov chain Monte Carlo.

The Markov property means that the state of a Markov chain transitions to another state with a probability that depends only on the most recent state of the system, and not its entire history. Monte Carlo sampling, on other hand, involves solving deterministic problems by repeated random sampling.Stan is a programming language designed to make statistical modeling easier and faster, especially for Bayesian estimation problems. That all sounds good, but why is that useful for me? Suppose you have a hierarchical ecological modeling problem with data clustered by space, time, and species, such as estimating the effect of ocean temperatures on coral growth.

Suppose just want to use informative priors to help fit a growth model. You can use Stan for that. Suppose you just prefer Bayesian analysis and want to run a simple multiple regression. Stan can do that. The purpose of this document is not to perfectly describe or debate Bayesian analysis, but to provide a path to get you started using Stan in your research.

This is not a deep dive into the inner workings of Stan and model fitting - explanations and examples are designed to try and help people get the hang of using Stan. This tutorial is intended as a bridge to help get people from zero to working in Stan.

Comments and suggestions are welcome! Computationally intensive read: can take a lot longer to run, like hours or days compared to seconds to minutes. Unfamiliar reviewers might not like it though you can and should push back on this one, but still, worth thinking about.

Stan is a programming language that allows you to write and fit models. This is unlike interpreted languages like R that let you more or less run code as you go. See how well those parameters fit the data Calculate the posterior probability of those parameters given your data, the likelihood, and priors.

Accept or reject the new parameters by some function proportional to how much the new parameters improve the model fit. This simple algorithm can be shown to always converge on an approximation of the posterior probability distribution of the model eventually. If you write the model, Stan has a number of built in algorithms for helping you use MCMC to fit and diagnose that model quickly and efficiently. A simple MCMC might choose a new parameter value by drawing from a multivariate normal distribution centered on the last parameter value, with some tuned or supplied covariance matrix.Compiling models takes time.

It is in our interest to avoid recompiling models whenever possible. If the same model is going to be used repeatedly, we would like to compile it just once. The following demonstrates how to reuse a model in different scripts and between interactive Python sessions.

Within sessions you can avoid recompiling a model by reusing the StanModel instance:. It is also possible to share models between sessions or between different Python scripts. We do this by saving compiled models StanModel instances in a file and then reloading it when we need it later. In short, StanModel instances are picklable. The following two code blocks illustrate how a model may be compiled in one session and reloaded in a subsequent one using pickle part of the Python standard library.

For those who miss using variables across sessions in R, it is not difficult to write a function that automatically saves a copy of every model that gets compiled and opportunistically loads a copy of a model if one is available. PyStan latest.Fit a model defined in the Stan modeling language and return the fitted result as an instance of stanfit. The path to the Stan program to use. The stan function can also use the Stan program from an existing stanfit object via the fit argument.

When fit is specified, the file argument is ignored. A character string either containing the model definition or the name of a character string object in the workspace.

This argument is used only if arguments file and fit are not specified. An instance of S4 class stanfit derived from a previous fit; defaults to NA. This is not a particularly important argument, although since it affects the name used in printed messages, developers of other packages that use rstan to fit models may want to use informative names.

A named list or environment providing the data for the model, or a character vector for all the names of objects to use as data. See the Passing data to Stan section below. A character vector specifying parameters of interest to be saved. The default is to save all parameters from the model. Logical scalar defaulting to TRUE indicating whether to include or exclude the parameters given by the pars argument. If FALSEonly entire multidimensional parameters can be excluded, rather than particular elements of them.

A positive integer specifying the number of iterations for each chain including warmup. The default is A positive integer specifying the number of warmup aka burnin iterations per chain. If step-size adaptation is on which it is by defaultthis also controls the number of iterations for which adaptation is run and hence these warmup samples should not be used for inference. The number of cores to use when executing the Markov chains in parallel.

The default is to use the value of the "mc.

However, we recommend setting it to be as many processors as the hardware and RAM allow up to the number of chains. See detectCores if you don't know this number for your system. A positive integer specifying the period for saving samples.

The default is 1, which is usually the recommended value. Unless your posterior distribution takes up too much memory we do not recommend thinning as it throws away information. The tradition of thinning when running MCMC stems primarily from the use of samplers that require a large number of iterations to achieve the desired effective sample size.

Because of the efficiency effective samples per second of Hamiltonian Monte Carlo, rarely should this be necessary when using Stan. Specification of initial values for all or some parameters. Can be the digit 0the strings "0" or "random"a function that returns a named list, or a list of named lists:. Let Stan generate random initial values for all parameters. The seed of the random number generator used by Stan can be specified via the seed argument.

If the seed for Stan is fixed, the same initial values are used. The default is to randomly generate initial values between -2 and 2 on the unconstrained support. Set inital values by providing a list equal in length to the number of chains. The elements of this list should themselves be named lists, where each of these named lists has the name of a parameter and is used to specify the initial values for that parameter for the corresponding chain.

Set initial values by providing a function that returns a list for specifying the initial values of parameters for a chain. See the Examples section below for examples of defining such functions and using a list of lists for specifying initial values. The seed for random number generation.

The default is generated from 1 to the maximum integer supported by R on the machine.

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