Kinda hate R, because, as much as it is a statistical dream, it is a programming nightmare? Is MATLAB too expensive when you try to run it on your cloud server farm and you’re anyway vaguely suspicious that they get kickbacks from the companies that sell RAM because otherwise why does it eat all your memory like that? Love the speed of C++ but have a nagging feeling that you should not need to recompile your code to do exploratory data analysis? Like the idea of Julia, but wary of depending on yet another bloody language, let alone one without the serious corporate backing or long history of the other ones I mentioned?
Python has a different set of warts to those other options. Its statistical library support is narrower than R - probably comparable to MATLAB. It is, however, sorta fast enough in practice, and nicer to debug, and support diverse general programming tasks well — web servers, academic blogs, neural networks, weird art projects, online open workbooks, and has interfaces to an impressive numerical ecosystem…
Although it is occasionally rough, it’s ubiquitous and free, free, free so you don’t need to worry about stupid licensing restrictions, and the community is enormous, so it’s pretty easy to answer any questions you may have.
But in any case, you don’t need to choose. Python interoperates with all these other languages, and indeed, makes a specialty of gluing stuff together.
Aside: A lot of useful machine-learning-type functionality, which I won’t discuss in detail here, exists in the python deep learning toolkits such as Tensorflow and Theano; you might want to check those pages too. Also graphing is a whole separate issue, as is optimisation.
In recent times, a few major styles have been ascendant in the statistical python scene.
scikit-learn exemplifies a machine-learning style,
with lots of abstract feature construction
and predictive-performance style model selection
built around homogeneously-typed (only floats, only ints)
matrices instead of dataframes.
This style wil be more familiar to MATLAB
users than to R users.
scikit-learn (sklearn to its friends) is the flagship of this fleet. It is fast, clear and well-designed. I enjoy using it for implementing ML-type tasks. It has various algorithms such as random forests and linear regression and Gaussian processes and reference implementations of many algorithms, both à la mode and passé. Although I miss sniff
glmnetin R for lasso regression.
SKLL (pronounced “skull”) provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features.
…provides a bridge between
sklearn’s machine learning methods and pandas-style Data Frames.
In particular, it provides:
a way to map DataFrame columns to transformations, which are later recombined into features
a way to cross-validate a pipeline that takes a pandas DataFrame as input.
“pystruct aims at being an easy-to-use structured learning and prediction library.”
Currently it implements only max-margin methods and a perceptron, but other algorithms might follow. The learning algorithms implemented in PyStruct have various names, which are often used loosely or differently in different communities. Common names are conditional random fields (CRFs), maximum-margin Markov random fields (M3N) or structural support vector machines.
xarray; what is this?
Notable application, ArviZ: Exploratory analysis of Bayesian models as seen in
Forecasting in python. Rob J Hyndman , in Python implementations of time series forecasting and anomaly detection recommends
tslearn is a Python package that provides machine learning tools for the analysis of time series. This package builds on (and hence depends on) scikit-learn, numpy and scipy libraries.
It integrates with other time series tools, for example:
It automatically calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.
Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions. Cesium has two main components - a Python library, and a web application platform that allows interactive exploration of machine learning pipelines. Take control over the workflow in a Python terminal or Jupyter notebook with the Cesium library, or upload your time-series files, select your machine learning model, and watch Cesium do feature extraction and evaluation right in your browser with the web application.
pyts is a time series classification library that seems moderately popular.
The non-python options in Forecasting are also worth looking at.
Interoperation with other languages, platforms
Direct API calls
You can do this using
%load_ext rpy2.ipython %R library(robustbase) %Rpush yy xx %R mod <- lmrob(yy ~ xx); %R params <- mod$coefficients; %Rpull params
Counter-intuitively this is remarkably slow. I have experienced much greater speed in saving data to the file system in one language then loading it in another. For that, see the next
via the filesystem
Much faster, weirdly, and better documented. Recommended. Try Apache arrow.
import pandas as pd import feather path = 'my_data.feather' feather.write_dataframe(df, path) df = feather.read_dataframe(path)
library(feather) path <- "my_data.feather" write_feather(df, path) df <- read_feather(path)
If that doesn’t work, try
protobuf or whatever.
There are many options.
hdf5 seems to work well for me.
See pycall from julia.
Use a known-good project structure
Local random number generator state
⚠️ this is out of date now; the new RNG API is much better.
Seeding your RNG can be a pain in the arse,
especially if you are interfacing with an external library
that doesn’t have RNG state passing in the API.
So, use a context manager.
Here’s one that works for
from numpy.random import get_state, set_state, seed class Seed(object): """ context manager for reproducible seeding. >>> with Seed(5): >>> print(np.random.rand()) 0.22199317108973948 """ def __init__(self, seed): self.seed = seed self.state = None def __enter__(self): self.state = get_state() seed(self.seed) def __exit__(self, exc_type, exc_value, traceback): set_state(self.state)
Exercise for the student: make it work with the default RNG also
Miscellaneous learning resources
agate is a stats package is not designed for high performance but for ease of use and reproducibility for non-specialists, e.g. journalists.
agate is a intended to fill a very particular programming niche. It should not be allowed to become as complex as numpy or pandas. Please bear in mind the following principles when considering a new feature:
- Humans have less time than computers. Optimize for humans.
- Most datasets are small. Don’t optimize for “big data”.
- Text is data. It must always be a first-class citizen.
- Python gets it right. Make it work like Python does.
- Humans lives are nasty, brutish and short. Make it easy
- hypertools is a generic dimensionality reduction toolkit. Is this worthwhile?
- Bonus scientific computation is also available through GSL, most easily via CythonGSL use and reproducibility for journalists.
- savez makes persisting arrays fast and efficient per default. Use that, if you are talking to other python processes.
- biopython is a whole other world of phylogeny and bio-data wrangling. I’m not sure if it adheres to one of the aforementioned schools or not. Should check.
- more options for speech/string analysis at natural language processing.